Direction-of-Arrival (DOA) estimation is a fundamental problem in signal processing. Diffuse sources, whose power density cannot be represented by a single angular coordinate, are typically characterized based on prior assumptions, which associate the source angular density with a specific set of functions. However, these assumptions can lead to significant estimation biases when they are incorrect. This paper introduces the Moment-Matching Estimation Technique (MoMET), a low-complexity method for estimating the mean DOA, spread, and power of a diffuse source without requiring prior knowledge on the source distribution. The unknown source density is characterized by its mean DOA and its first central moments, which are estimated through covariance matching techniques. The MoMET parameterization is robust to incorrect model assumptions, only requires the optimization of the mean DOA, and applies to both Uniform Linear Arrays (ULAs) and non-ULAs as well as symmetric and asymmetric sources. The asymptotic bias and covariance of the new estimator are derived and its performance is demonstrated through simulations.

P band synthetic aperture radar (SAR) tomography represents a powerful tool for characterizing the 3-D structure of tropical forests from their electromagnetic response. Current techniques separate the responses of the forest canopy and the underlying ground using SAR tomography, polarimetric diversity, and complex processing techniques. This letter shows that similar performance and better stability may be achieved using single-polarization data and parametric tomographic focusing, performed using a low-dimensional model. The vertical density of reflectivity of a tropical forest is modeled, at P band, using a Dirac function for the ground and a narrow peak for the volume. The performance of the proposed method is evaluated using P band tomographic data acquired during the TropiSAR campaign, and the results show that it can accurately and reliably estimate key structural parameters of the observed topical forest.

Exploring vertical forest structures worldwide via remote sensing faces challenges. Recent technologies like waveform light detection and ranging (LiDAR) from NASA’s global ecosystem dynamics investigation (GEDI) and SAR tomography (TomoSAR) from future European Space Agency (ESA) BIOMASS offer promising solutions. This article assesses the performance of spaceborne GEDI and TomoSAR airborne data from an ESA’s TomoSense campaign to highlight the important role of GEDI measurements in BIOMASS algorithm training and establishing precise site-specific processing parameters. Our study in Germany’s Eifel National Park delves into the precision of GEDI and P-band TomoSAR in measuring surface [digital terrain model (DTM)] and vegetation [canopy height model (CHM)] heights. Results demonstrate that GEDI and P-band TomoSAR offer high-resolution and precise surface and vegetation heights and vertical profile measurements. While GEDI relative height (RH) at 98% (RH98) was previously recommended for tropical forests, our findings advocate for RH85 as the optimal metric for temperate forests. The research supports improving the accuracy of both DTM and CHM utilizing GEDI beams with full-power lasers coupled with high sensitivity and signal-to-noise ratio (SNR). Ground elevation measurements are more accurate than canopy height estimates for temperate forests, with DTM RMSE about 2 m and CHM RMSE about 3 m for GEDI and TomoSAR measurements. By analyzing the vertical structure of monthly GEDI data, we note a 1-m shift in the volume peak between GEDI’s leaf-on and leaf-off periods. At the same time, TomoSAR consistently exhibits a lower volume peak by about 2 m compared to GEDI during leaf-on seasons. In conclusion, our research underscores the complementary roles of TomoSAR and GEDI in accurately mapping diverse forest types, thereby bolstering the effectiveness of the BIOMASS mission.

Tomographic SAR (TomoSAR) at low frequency, i.e., P/L band, has become a promising tool for forest structure study. Forest canopy height and underlying topography are two of the most important parameters one can estimate using TomoSAR technique. One simple way to estimate these two parameters is to detecting the peaks of the tomographic profile, which, however, can lead to large biases due to complicated forest structure, sidelobes or insufficient TomoSAR resolution. Polarimetric TomoSAR (Pol-TomoSAR) provides a solution to this by exploring the polarimetric diversity to separate the ground and canopy components and then conduct independent TomoSAR analysis. However, Pol-TomoSAR technique suffers from low ground-to-volume ratio (GVR), which often leads to unsuccessful ground and canopy separation. To mitigate this propblem, in this paper, we provide a deep-learning solution to ground and canopy height estimation from 3D tomographic profile through the identification of the patterns of ground and canopy peaks. A 3D U-net model is introduced in our solution to grasp as much three-dimensional characteristics of the tomographic profile as possible. Moreover, our model can be well trained using only synthetic TomoSAR dataset, making it easy to implement when we don’t have enough real data with LiDAR references. The proposed method is validated on P-band real TomoSAR dataset from multiple test sites in AfriSAR campaign, showing that it can achieve more accurate ground and canopy height estimation than the state-of-the-art Pol-TomoSAR techniques. The maximum RMSE improvement reaches as high as 66.4% and 63.2% for ground and canopy top height, respectively.

In this paper, we propose an unsupervised statistical approach for near real-time monitoring of forest loss, leveraging Bayesian inference. We address the identification of forest loss as a change-point detection problem within non-filtered Sentinel-1 single polarization time series data. Each new observation contributes to the probability of deforestation occurrence, utilizing prior knowledge and a data model. Our method offers the advantage of detecting small-scale deforestation without resorting to spatial filtering techniques, thus preserving the native spatial resolution of the Sentinel-1 measurements. To assess its effectiveness, we conducted comparative evaluations against existing operational deforestation monitoring systems. The validation campaign revealed that our method exhibits enhanced detection performance with low false alarm rates with respect to existing systems across diverse landscapes, including dense forest regions such as the Brazilian Amazon, as well as seasonality-dependent areas like the Cerrado, which is strongly under-monitored by existing technology. This robustness stems from the sequential adaptive process inherent in our approach, which enables effective monitoring even in the presence of backscatter variations.

Synthetic Aperture Radar (SAR) tomography has been successfully applied to the characterization of forest using 3D imaging. Nevertheless, the information content extracted by this technique is limited by the resolution in range and elevation. Also, it is demonstrated that a small number of parameters allows the reconstruction of tomograms that are close to the measured ones. In this paper, forest tomograms are reconstructed by an inversion method using forest scattering models with few parameters. Key forest parameters, such as ground position zg, forest height hvand the ratio between ground intensity and volume are determined using mono-polarized P-band data. These results are compared for different types of forest scattering models with verification data from the TropiSAR and TomoSense campaigns.

This paper studies the potential of X-band space borne SAR tomography for characterizing the structure of snow packs. The proposed solution is based on a constellation of 4 or 5 small satellites operated in a specific MIMO-FDM configuration, whose geometrical and spectral features are optimized so as to reach vertical resolution and ambiguity figures equivalent to 25 monostatic sensors. Estimation performance bounds, computed for specific scenarios, show that this concept is able to accurately capture the internal structure of shallow or deep snow-packs from a single measurement and to unambiguously estimate Snow Water Equivalent using two observations, with a very high accuracy and without assumptions on the structure or thickness of the measured cover.

This paper presents a method for multi-layered snowpack vertical profile characterization using a low complexity portable MIMO bistatic radar system. An iterative procedure based on calculating the electromagnetic distance which takes into account the refractive index of each layer is proposed. It estimates the refractive index from the snowpack top to the bottom layers. Furthermore, experimental radar measurements performed at the Col de Porte, in the french Alps, is backed up by in-situ stratigraphic samplings provided by Météo France and which are used as a reference. The obtained results are anaylsed and discussed.

The polarimetric time-frequency coherent indicator has been proposed and effectively applied, especially for target detection. In this paper, based on the complex Wishart distribution, an associated asymptotic probability for the statistical test of the polarimetric time-frequency coherence is analytically derived. The goodness of fit for the probability density function is tested with Monte Carlo simulated data. It can be observed that the estimated probability density function curve fit well with the theoretical value.

Our study highlights the important role of GEDI measurements in BIOMASS algorithm training and the establishment of precise site-specific processing parameters. Combining GEDI measurements at sparse coordinates and SAR tomography (TomoSAR) estimates enables the creation of detailed canopy height maps (CHM). While relative height (RH) at 98% (RH98) was previously recommended for tropical forests, our findings advocate for RH85 as the optimal metric for temperate forests. Emphasis is placed on selecting shots with over 90% sensitivity for ground return detection and GEDI beams equipped with full-power lasers. Additionally, we show the GEDI profile data’s unique capacity to investigate annual changes, revealing significant volume contributions during leaf-on periods and increased ground importance during leaf-off seasons.

This paper introduces a novel approach in pavement inspection by applying high-resolution imaging techniques onto a Constant Offset Sliding Bistatic Ground-Based Synthetic Aperture Radar system operated in forward-scattering mode. The performance of this configuration, known for its minimal complexity, is significantly improved by incorporating the spectral analysis method. The integration enhances vertical discrimination and proves effective in complex scenarios with fewer actual sources than anticipated. This approach represents a alternative solution in non-destructive road infrastructure analysis, offering a powerful tool for precise pavement condition monitoring.

This work investigates a new concept to finely resolve the vertical structure of natural media, like snow, ice, vegetation, by using a formation of spaceborne Synthetic Aperture Radars (SAR) mounted onboard different satellites. The formation is assumed to operate in Multiple Input Multiple Output (MIMO) mode by implementing a Frequency Division Multiplexing (FDM) access scheme, where all satellites transmit simultaneously on different frequency bands and receive the echoes scattered by the Earth’s surface in all transmitted bands. In so-doing, a formation onNsatellites is used to produceN2SAR images. By the principle of Diffraction Tomography, each of these images represents a distinct set of wavenumbers, i.e. a distinct region of the spatial spectrum of the observed scene. The vertical separation between any two sets of wavenumbers defines the interferometric differential wavenumber, which determines the sensitivity of that particular pair to the vertial structure of the observed scene. Fine vertical resolution is achieved by developing a novel approach to set the satellite positions in such a way that the resulting interferometric differential wavenumbers form an almost uniformly-spaced array of maximum length under the constraint of a given height of ambiguity and interferometric coherence magnitude. As a result, we show two examples where formations of 4 or 5 satellites are deployed to provide the equivalent of 17 and 26 monostatic acquisitions, respectively. Such figures are comparable to the best airborne and ground-based systems available as of today, and indicate the concrete possibility to image the vertical structure of natural targets from space at fine resolution. The concept here developed to deploy the formation is referred to as Minimum Redundancy Wavenumber Illumination (MRWI), as it is shown to be a generalization to distributed targets of the principle of Minimum Redundancy Virtual Array (MRVA) used in array theory. The analysis is supported by results from synthetic data generated by numerical simulations.

With Synthetic Aperture Radar tomography, it is possible to reconstruct reflectivity profiles in the direction orthogonal to the line-of-sight. When only a small number of interferometric baselines is available, the spatial resolution of profiles produced by beamforming is insufficient. While many iterative algorithms have been proposed in the past years to achieve improved tomographic reconstructions, these methods often require a large computational cost. In this paper we explore the use of a light-weight neural network to dramatically accelerate tomographic reconstruction in anticipation of the deluge of data generated by the future BIOMASS satellite.

The CIRFA-Cruise 2022 with RV Kronprins Haakon to the north-eastern coast of Greenland in the period April 22nd to May 9th 2022 was organised to perform measurements and make observations which allow for validation of sea ice remote sensing information and forecast products resulting from work in the Centre for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA), a Centre for Research-based Innovation at UiT the Arctic University of Norway. This paper uses data collected during the cruise to investigate questions related to the interpretation and temporal consistency of polarimetric features computed from a series of quad-pol Radarsat-2 (RS-2) images, which was collected over a fast ice site in the Belgica Bank area in the western Fram Strait. The CIRFA-2022 Cruise team visited this fast ice site in the end of April 2022. The time series covers the transition from cold winter conditions in April to melting in mid June. This transition impacts radar backscat-tering, as can be clearly seen in the Pauli decomposition of quad-pol images.

This paper presents some results obtained during the 2022 CIRFA arctic cruise concerning the measurements of the radar response of different types of sea-ice using a high-resolution Ground-Based radar system operating at C band. This MIMO device was able to directly measure tomograms, or slices of reflectivity in the elevation-ground plane, allowing to quantitatively appreciate the penetration of radar waves into sea-ice types having complex geometrical features, and to assess the dominant contributions measured by radar devices at C band.

Quantification of forest’s vertical structure in the tropics using remote sensing is a challenge. NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne LiDAR data, whereas the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. We show that GEDI and P-band TomoSAR can directly measure vegetation heights and vertical profiles with high resolution and precision. The GEDI vegetation height error is 5 m at the tropical sites, similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e., RH98 from full power shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.

This paper investigates the potential for accurate retrieval of Snow Water Equivalent (SWE) using an across-track formation of Synthetic Aperture Radar (SAR) satellites.

Synthetic aperture radar tomographic imaging reconstructs the three-dimensional reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration. In forest areas, a high number of elements backscatter the radar signal within each resolution cell. To reconstruct the vertical reflectivity profile, state-of-the-art techniques perform a regularized inversion implemented in the form of iterative minimization algorithms. We show that light-weight neural networks can be trained to perform this inversion with a single feed-forward pass, leading to fast reconstructions that could better scale to the amount of data provided by the future BIOMASS mission. We train our encoder-decoder network using simulated data and validate our technique on real L-band and P-band data.

Measuring the vertical structure of tropical forests using remote sensing technology is challenging. To overcome this, active sensors, such as P-band Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR), are used to penetrate thick vegetation layers. NASA’s Global Ecosystem Dynamics Investigation (GEDI) uses spaceborne LiDAR data. In contrast, the European Space Agency’s (ESA) BIOMASS mission uses multiple acquisitions of SAR data to create 3D images through a technique called SAR tomography (TomoSAR). The paper discusses the forest’s vertical structure, such as volume peak (or volume scattering center), penetration, and reflectivity, using GEDI and airborne P-band TomoSAR by analyzing measurements at tropical forest sites in South America and Africa. It was found that the location of the volume peak in TomoSAR is consistently lower than in GEDI, with a range of 2-4 m depending on the polarization and the height of the forest layers. Compared to GEDI, TomoSAR data has a better ground reflection for vegetation taller than 25 m. GEDI and TomoSAR data can accurately capture vertical information in the canopy levels (between 10-40 m), displaying a strong correlation in the volume layers. The highest correlation occurs around 30 m above ground level, aligning with previous research in developing algorithms for the BIOMASS mission in aboveground biomass retrieval. Together, TomoSAR and GEDI are robust and comparable in studying tropical forests and support the BIOMASS mission for global biomass mapping.

SAR tomography represents a unique way to characterize forested areas from their 3-D density of reflectivity. As shown by numerous studies, coherent 2-D SAR images with intermediate horizontal resolution may be processed through spectral analysis techniques in order to focus 3-D cubes of reflectivity, and to provide an electromagnetic description of forets which is generally sufficient to estimates its main features, such as underlying ground topography, tree height and above-ground biomass. Nevertheless, a refined analysis of the vertical structure of a forest is usually highly limited by the vertical resolution and by the presence of side-lobes and focusing artifacts whose separation from the actual response may be problematic. As shown in this paper, direct deconvolution of the vertical impulse response using high-resolution techniques is an underdetermined inverse problem with an infinite number of plausible solutions. The use of basis functions, such as those proposed by Aguilera et al. [1], as well as a set of signal properties likely to be well adapted to the reflectivity of forested areas, allows to drastically reduce the size of the solution domain. This paper iuses an efficient iterative technique to estimate the intrinsic vertical reflectivity profile of forest measured at L and P bands

3-D imaging using SAR tomography is a well-recognized technique for the characterization of forested areas. Studies revealed that the intensity of radar echoes originating from specific locations within the canopy of forest could be used to estimate its above ground biomass. Moreover, a recent work proposed an estimation technique using a pair of interferometric SAR images only. The images are combined in order to cancel contributions from the ground, and to roughly estimate the volume reflectivity. This paper proposes to study the influence of temporal decorrelation of this minimalist approach, which relies on the hypothesis of perfectly correlated signals. A model, based on second order statistics, is proposed and is used to predict the influence of temporal decorrelation of the relative error of the above ground biomass estimation over tropical forests measured at$\mathrm{P}$band.

Estimating tropical forests vertical structure using remote sensing is a challenge. Active sensors such as low-frequency Synthetic Aperture Radar (SAR) operating at P-band, with a wavelength of ~ 69 cm wavelength, and Light Detection and Ranging (LiDAR) are able to penetrate thick vegetation layers. While NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne liDAR data, the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. Our study shows the potential value of GEDI and TomoSAR acquisitions in producing accurate estimates of forests vertical structure. By analyzing airborne P-band TomoSAR, airborne LiDAR, and spaceborne GEDI LiDAR at a tropical forest site in Paracou, French Guiana, South America, we show that both GEDI and P-band TomoSAR can directly measure surface, vegetation heights, and vertical profiles with high resolution and precision. Airborne TomoSAR is of higher quality than GEDI due to better penetration properties and precision. However, the GEDI vegetation height root-mean-square error is less than 5 m, for an average forest height value around 30 m at the Paracou site, which is similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e. shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.

Spaceborne synthetic aperture radar (SAR) has been used for years to estimate high-resolution surface wind field from the ocean surface backscattered signal. Current SAR platforms have one single fixed antenna, and traditional inversion/retrieval schemes rely on one copolarized channel, leading to an unconstrained optimization problem for providing independent estimates of wind speed and direction. For routine application, this is generally solved witha prioriinformation from the numerical weather prediction (NWP) model, inducing severe limitations for rapidly evolving meteorological systems where discrepancies can be significant between model and measurements. In this study, we investigate the benefit of having two simultaneous acquisitions with phase-preserving information in copolarization and cross polarization provided by Sentinel-1 (S-1). A comprehensive analysis of the co-cross-polarization coherence (CCPC) is performed to adequately estimate and calibrate CCPC values from S-1 interferometric wide (IW) mode images acquired over the ocean. A new polarimetric calibration (PolCAL) methodology based on least-squares (LS) criterion and direct matrix inversion is proposed yielding crosstalk estimates. We document CCPC odd symmetry with respect to relative wind direction for light to medium wind speeds (up to 14 m/s) and incidence angle from 30° to 45°. The azimuthal modulation is found to increase with both wind speed and incidence angle. An analytical model C-band polarimetric geophysical model function (CPGMF) is provided. The synergy of the CCPC with other radar parameters, such as backscattering coefficients or Doppler, to further constrain the inversion scheme is assessed, opening new perspectives for SAR-based wind field retrieval independent of any NWP model information.

This paper addresses some advanced PolSAR approaches combined with time-frequency and tomographic techniques for characterization of urban areas. Three case studies are dedicated to demonstrate the effectiveness of these polarimetric approaches, using spaceborne SAR data such as Sentinel-1, RadarSAT2 and TerraSAR/Tandem-X. The results show advanced PolSAR approaches can provide a refined characterization of urban areas from space.

This aims to compare the performance of different observations modes of the future BIOMASS mission for the characterization of tropical forests. In particular, it provides indicators of variability for different typical descriptors of the SAR response of a forest, and computes estimates using real data acquired at P band in the frame of the TropiSAR campaign. Results show that the best performance, reached in the Polarimetric and Tomographic mode, degrades slightly in the single-polarization case and significantly when the vertical tomographic resolution is reduced. Nevertheless, it is shown that in this latter case, the use of priors estimated in the high-resolution tomographic phase and consisting on the estimate of the wave extinction, leads to a spectacular improvement of the performance. This results reveals important in the frame of the BIOMASS missions which plans to phases with different acquisition configurations.

This paper proposes to use ground-based high-resolution 3-D radar imaging, in order to study the interaction between electromagnetic waves and snow packs, and to provide a physical interpretation for the reflectivity of Sentinel 1 images over snow covered regions. Preliminary results show that, according to usually assumed behaviors, fresh snow has an extremely low reflectivity at C band, and wet snow does not let waves go through. Between these two extreme configurations, this study reveals that snow packs may have complex and significant scattering patterns, mainly due to the presence of transformed snow and of rough interfaces between the layers, again due to transformation phenomena.

This paper deals with the retrieval of Digital Terrain Model (DTM) from P-band SAR, with a specific focus on the Paracou study case acquired during the TropiSAR airborne campaign. A technique based on the sum of Kronecker product (SKP) decomposition is used to compute a SAR-derived DTM. An analysis of the differences between this estimate and the one obtained for airborne LIDAR acquisitions is proposed. Moreover, a multi-scale adaptive filtering method, using local regression and morphological image processing, is also proposed to improve the DTM. The post-processed DTM show an improvement of the estimation of ground topography.

Polarimetric Synthetic Aperture Radar Tomography (TomoSAR) is a technology to image the three-dimensional (3D) structure of the illuminated media. TomoSAR exploits the key feature of microwaves to penetrate into vegetation, snow, and ice, hence providing the possibility to see features that are hidden to optical and hyper-spectral systems. Several experimental studies by different research groups demonstrate that the use of the 3D information results in an accurate characterization of forested areas, providing access to a number of biophysical variables such as terrain topography below the vegetation, forest height, forest Above Ground Biomass (AGB), and forest classification. This paper is intended to provide the reader with an introduction to the use of TomoSAR for the characterization of forest areas, addressing basic imaging principles and methods, retrieval of biophysical parameters, and perspective for spaceborne missions.

This paper investigates and compares different imaging configurations for roadway inspection. Ground-penetrating radar is generally used to detect and characterize the vertical structure of the underground medium, whereas Off-Nadir Synthetic Aperture Radar can work both in back-scattering and forward-scattering modes. Back-scattering mode provides a capacity of discrimination in azimuth and range directions, while forward-scattering mode converts range resolution into vertical resolution. Vertical sampling is then used to realize tomography. 3D-focusing imaging of roadways is thus shown to be able to adequately and efficiently evaluate the underground structure.

P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden. BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%-36% (27-33 t/ha) for airborne data and 38%-39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity.

This article addresses the 3-D reconstruction of urban areas using a minimal number of Synthetic Aperture Radar (SAR) acquisitions, that is, a set of three images, characterized by intermediate spatial resolution features. In such extreme conditions, conventional tomographic techniques reveal unadapted to refined 3-D imaging purposes, either due to the resulting intrinsic coarse vertical resolution, or to the low dimensionality of the data set, that prevents any separation of complex mixed scattering patterns. A new high-resolution (HR) tomographic estimator, based on a polarimetric signal subspace fitting criterion, is proposed to overcome these limitations, as this method adapts to the statistical behavior of the backscattered signals using robust metrics. The optimization of the corresponding focusing criterion is led through a new polarimetric alternating projection algorithm, characterized by a low computational cost, which may also be used to optimize the polarimetric the deterministic maximum likelihood criterion. The proposed polarimetric signal subspace fitting technique is shown to outperform the other studied HR techniques over both simulated signals and data acquired by the DLR’s ESAR sensor at L-band over Dresden city, Germany. Finally full-rank polarimetric tomographic estimators are proposed that generalize nonparametric polarimetric estimators, and permit to estimate second-order polarimetric representations in 3-D, instead of unitary rank target vector with their conventional versions. This approach makes it possible to characterize polarimetric scattering mechanisms in 3-D.

This paper studies ground/volume and ground/trunk double-bounce scattering mechanisms occurring within a vegetated medium. Polarimetric Synthetic Aperture Radar data, acquired over different reduced-scale scenes, are used to isolate scattering mechanisms and evaluate their polarimetric features. It is found that ground/volume double-bounce is diffuse and strongly affects the retrieved ground patterns, with ground-projected contributions occupying a wide polarimetric space, which cannot be filtered out using polarimetric diversity. Whereas, the ground/trunk double-bounce is coherent, spatially well localized and can be easily filtered out. It is therefore shown that the assumption of ground response with a null HV polarization even for a smooth ground is not valid when a volume lies above it.

This paper presents experimental results of the 3-D characterization of alpine snowpacks, obtained using a low complexity portable MIMO radar system that operates at C-band. Different types of snow at different altitudes and seasons are studied. The acquired datasets are processed using the Back Projection Algorithm and the resulting tomograms are compared to Météo France ground measurements (Snow Micro Pen transects, density and stratigraphy profiles and liquid water content). The obtained tomograms show that the system mostly detects melt forms and faceted crystals. These measurements provide 3-D electromagnetic ground truth that can be used to confirm the results obtained by Sentinel-1.

This paper addresses multiple scatterer detection over man-made areas using SAR tomography. Diverse high-resolution tomographic estimators are compared, showing that stochastic maximum likelihood and signal subspace fitting techniques are more efficient and accurate than deterministic maximum likelihood technique when estimating coherent scatterers. Based on these two techniques, detection schemes are proposed and their effectiveness for scatterer detection is demonstrated by using multi-baseline L-band SAR data over a test site containing man-made objects .

This paper evaluates the potential of Polarimetric SAR Tomography (PolTomSAR) for analyzing a semi-opaque Random Volume lying over a rough Ground (RVoG), and to assess different characterization methods aiming to retrieve the ground characteristics. This study is based on the use of controlled experiments, during which a miniaturized RvoG-like scene is imaged with a laboratory 3-D SAR, operated along a 2-D aperture. Polarimetric and tomographic signals are acquired for various configurations, and different scattering mechanisms are naturally captured by removing, hiding or replacing different parts of the scene. This unique possibility to isolate specific scattering mechanisms is used to evaluate the performance and relevance of existing decomposition techniques generally applied to forest characterization.

The availability of multi-temporal stacks of SAR images opens the way to new speckle reduction methods. Beyond mere spatial filtering, the time series can be used to improve the signal-to-noise ratio of structures that persist for several dates. Among multi-temporal filtering strategies to reduce speckle fluctuations, a recent approach has proved to be very effective: ratio-based filtering (RABASAR). This method, developed to reduce the speckle in multi-temporal intensity images, first computes a "mean image" with a high signal-to-noise ratio (a so-called super-image), and then processes the ratio between the multi-temporal stack and the super-image. In this paper, we propose an extension of this approach to polarimetric SAR images. We illustrate its potential on a stack of fully-polarimetric images from RADARSAT-2 satellite.

This paper presents a new approach for the study of ship discrimination in complex sea ice ocean environment using single polarimetric synthetic aperture radar data and sub-spectra strategy. A statistic descriptor related to the signal coherence in the Time-Frequency domain, is proposed to enhance the ship/background contrast and improve discrimination capabilities. Using RADARSAT-2 single polarization data over complex sea ice scenes in Arctic ocean, experimental results demonstrate the efficiency of this method in terms of ship location retrieval and response characterization.

Polarimetric synthetic aperture radar tomography (Pol-TomoSAR) allows to achieve a 3-D characterization over urban areas using multiple polarimetric acquisitions. However, using spaceborne datasets, such as TerraSAR-X, it is difficult to localize the distributed or uncorrelated scattering patterns along elevation due to the temporal decorrelation. In order to overcome this limitation, this paper proposes polarimetric correlation tomographic techniques based on Tandem-mode images. The key of this technique is to build a covariance matrix from the observed Tandem coherence pairs, and then apply conventional covariance-based tomographic techniques. This processing allows to extract both coherent and distributed scatterers. The resulting 3-D reconstruction is more refined and detailed, compared to the one derived from TerraSAR-X data. Seven TSX/TDX pairs in fully polarimetric mode over a small county in Yunnan province, China, are used to demonstrate the effectiveness of this technique for the characterization of urban environments.

This paper concerns processing techniques for the the characterization of a tropical forest using PolTomSAR data at P band. In particular, existing forest biomass estimation methods, relating biomass to sampled tomographic intensity values, are revisited using simple methodological step-sand an adaptive tomographic intensity sampling approach. The canopy reflectivity sampling location is determined as a function of the effective forest height and of the tomographic resolution, in order to compensate geometrical mismatches. Moreover, an adaptive polarimetric decomposition technique is used to further decouple the sampled intensity from ground and topographic tomographic scattering effects. The performance of the proposed techniques is assessed using TROPISAR P-band data acquired by the ONERA's SETHI sensor over the Paracou data site in French Guiana in 2009. Results indicate over this site a substantial reduction of the Above Ground Biomass (AGB) estimation error, with respect to existing techniques.

This paper summarizes a set of studies led on the topic of polarimetric coherence optimization for the coherent processing of stacks of polarimetric SAR images. It is shown that coherence maximization may be understood differently depending on the application at hand. Extracting polarimetric coherent signals embedded in noise or in a severe background requires to use polarimetric diversity as a supplementary mean for discovering organized speckles patterns, whereas classical MB-PolinSAR coherence optimization gives more importance to the polarimetric scattering mechanisms that extremize coherence values. This paper reviews different techniques able to cope with an arbitrary number of images and that are characterized by their low degree of computational complexity, conferred by the favored use of analytical solutions. The usefulness of these techniques is demonstrated using various kinds of applications to real spaceborne and airborne data sets.

The objective of this paper is to provide a better understanding of tomographic capabilities to estimate above ground biomass (AGB) in dense forested areas at P-band. The analysis is carried out on airborne data acquired over sites in French Guyana and in Gabon during the ESA campaigns TropiSAR and AfriSAR 2015, respectively. Over both sites, P-band tomography allows us to retrieve the vertical structure of the forest, to better characterize the ground and/or volume scattering mechanisms and to provide a unique solution for the AGB retrieval over the full range of biomass. The relationship between AGB and tomography data was found to be highly similar for forests across continents and sites: Paracou (French Guiana), Lope, Rabi and Mondah (Gabon). The developed metrics derived from the tomographic data have been found highly correlated to reference in situ AGB estimates (R2=0.85) and the root mean square error was 16% (for AGB ranging from 0 to 500 t/ha). These results have strong implications for the tomographic phase of the BIOMASS spaceborne mission.

The objective is to make a review of the current status of the PolSARpro-Bio software: the new scientific toolbox for ESA & third party fully polarimetric SAR missions. The objective of this current project is to provide an Educational Software that offers a tool for self-education in the field of Polarimetric SAR data analysis and a comprehensive suite of functions for the scientific exploitation of fully and partially polarimetric data sets. The PolSARpro-Bio software establishes a foundation for the exploitation of polarimetric techniques for scientific developments and stimulates research and applications developments using Pol-SAR, Pol-InSAR, Pol-TomoSAR and Pol-TimeSAR data.

A tomographic synthetic aperture radar (TomoSAR) represents a possible route to improved retrievals of forest parameters. Simulated orbital L-band TomoSAR data corresponding to the proposed Satellites for Observation and Communications-Companion Satellite (SAOCOM-CS) mission (1.275 GHz) are evaluated for retrieval of above-ground biomass in boreal forest. L-band data and biomass measurements, collected at the Krycklan test site in northern Sweden as part of the BioSAR 2008 campaign, are used to compare biomass retrievals from SAOCOM-CS to those based on SAOCOM SAR data. Both data sets are in turn compared with the corresponding airborne case, as represented by experimental airborne SAR through processing of the original SAR data. TomoSAR retrievals use a model involving a logarithmic transform of the volumetric backscatter intensity, Ivol, defined as the total backscatter originating between 10 and 30 m above ground. SAR retrievals are obtained with slope-compensated intensity γ0using the same model. It is concluded that tomography using SAOCOM-CS represents an improvement over an airborne SAR imagery, resulting in biomass retrievals from a single polarization (HH) having a 26%-30% root-mean-square error with a little to no impact from the look direction or the local topography.

In this paper, the retrieval of sea-ice surface heights from the interferometric TanDEM-X data is investigated. The data were acquired over fast and drifting ice in Fram Strait located between Greenland and Svalbard. Additional measurements of the sea-ice surface topography were carried out using a stereo camera and a laser altimeter. The comparison of the surface elevation retrieved from TanDEM-X imagery with the results of the stereo camera measurements revealed that sea-ice ridges greater than 0.5 m can be estimated with a root-mean-square error of 0.3 m or less with the error decreasing as a function of ridge height. Although the helicopter-borne laser data are only available as 1-D profiles with a much higher across-track spatial resolution than the TanDEM-X data, they proved to be useful for the validation. The need for multilook averaging to reduce the phase noise is identified as the main challenge in achieving the spatial resolution necessary for retrieving sea-ice surface topography using synthetic aperture radar interferometry.

This paper proposes different processing techniques for the polarimetric 3-D imaging of forested areas using multi-baseline interferometric SAR data, acquired in tandem configuration from spaceborne SAR sensors. Tandem-like acquisition modes, based on the simultaneous measurement of interferometric pairs, represent a high-potential alternative for the tomographic imaging of scenes with rapidly decorrelating scattering features using a spaceborne SAR. The counterpart related to this independent interferometric sampling lies in the restricted amount of available information, whose processing requires specific techniques. These methods as well as their potential for boreal forest characterization are evaluated in the frame of the preparation of the SAOCOM CS mission using ESA's BIOSAR II campaigna data sets acquired at L band by the DLR ESAR sensor.

TomoSAR provides 3D information of complex targets such as forests. Space-borne SAR data utility for TomoSAR analysis is limited due to temporal decorrelation. A novel technique is introduced which utilizes multiple TanDEM-X data sets to generate accurate tomograms. The technique is demonstrated using multiple space-borne SAR acquisitions over Indian tropical forest. Cross-validation with PolInSAR estimation forest height and field height shows high accuracy of generated tomograms. Fully polarimetric space-borne X-band SAR data shows surprising capability to extract 3D scattering information of forested regions.

The objective of this paper is to provide a better understanding of tomographic capabilities in characterization of dense forested areas at P-band. The analysis is carried out on airborne data acquired by ONERA over the site in French Guyana, and in Gabon during the ESA campaign TropiSAR 2009 and AfriSAR 2015, respectively. The results shown support the idea that ground- and -volume interactions play a significant role at P-band. For a dense forest of 30 m and more, strong ground contribution at P-band can be visible in tomograms. P-band tomography allow us to retrieve the whole forest vertical structure, better characterizing of the ground and/or volume scatterings and providing an unique solution in high biomass ranging from 0–500 t/ha.

Starting from a stack of co-registered SAR images in interferometric configuration, SAR tomography performs a reconstruction of the reflectivity of scatterers in 3-D. Several scatterers observed within the same resolution cell of each SAR image can be separated by jointly unmixing the SAR complex amplitude observed throughout the stack. To achieve a reliable tomographic reconstruction, it is necessary to estimate locally the SAR covariance matrix by performing some spatial averaging. This necessary averaging step introduces some resolution loss and can bias the tomographic reconstruction by mistakenly including the response of scatterers located within the averaging area but outside the resolution cell of interest. This paper addresses the problem of identifying pixels corresponding to similar tomographic content, i.e., pixels that can be safely averaged prior to tomographic reconstruction. We derive a similarity criterion adapted to SAR tomography and compare its performance with existing criteria on a stack of Spotlight TerraSAR-X images.

In this work we discuss the differences between SAR tomographic analyses produced by direct 3D focusing, which we refer to as coherent tomography, and by processing simultaneous interferometric pairs collected in different passes, which we refer to as incoherent tomography. While the application of coherent tomography using space-borne sensors is often hindered by temporal decorrelation, incoherent SAR tomography appears to be a viable solution upon the condition that simultaneous InSAR pairs are available, which is possible by using bistatic SAR systems. For this reason, this paper is focused on assessing the capabilities of incoherent tomography, and on discussing the implications for bistatic SAR systems. Examples are shown based on simulated SAOCOM-CS data.

Despite its ability to characterize 3-D environments, synthetic aperture radar (SAR) tomographic imaging, when applied to the characterization of targets concealed beneath forest canopies, may appear as an ill-conditioned estimation problem, with a complex mixture of numerous scattering mechanisms measured from a few different positions. Among the set of tomographic estimators that may be used to characterize such complex scattering environments, nonparametric tomographic techniques are more robust to focus on artifacts but limited in resolution and, hence, may fail to discriminate objects, whereas parametric ones provide better vertical resolution but cannot adequately handle continuously distributed volumetric scattering densities, characteristic of forest canopies. This letter addresses a new wavelet-based sparse tomographic estimation method for the 3-D imaging and discrimination of underfoliage objects that overcomes these limitations. The effectiveness of this new approach is demonstrated using L-band airborne tomographic SAR data acquired by the German Aerospace Center over Dornstetten, Germany.

This paper presents new experimental results of 3-D imaging using tomographic techniques over a snow covered sea ice medium, sensed with an X-band radar system. The available data are from a ground-based synthetic aperture radar data collection campaign carried out over Kattfjord, Tromsø, Norway. Direct imaging of the vertical structures of the radar reflectivity of the snow and sea ice layers is achieved by focusing the signal from a 2-D synthetic array in the 3-D space. The effect of a change in propagation velocity of the wave inside the considered medium is investigated in the focusing process, and the tomograms are effectively corrected for this effect. The distribution of the scattering contributions in the vertical direction reveals a strong response from the sea ice cover. Tomograms at two different polarizations are investigated and compared. The results and the interpretations are also supported by the simulated data from the same system.

This paper presents new principles and techniques to perform High Resolution (HR) 3-D imaging of volumetric environments using Polarimetric SAR Tomography (POLTOMSAR) and Multi-Baseline Polarimetric and Interferometric SAR (MB-POL-inSAR) data. Unlike classical polarimetric spectral estimation approaches which consider polarization as way to improve the discrimination between vertically aligned scatterers, or to estimate unitary rank polarimetric scattering features [1] [2], this paper provides full rank techniques which permit to estimate 3-D coherency matrices that can be characterized using classical polarimetric processing algorithms. The algorithms investigated here, Beamformer, Capon and MUSIC, have a relatively low numerical complexity and varying levels of resolution. A novel approach is developed to estimate 3-D full rank polarimetric covariance matrices with HR spatial properties.

SAR imaging of concealed targets beneath the canopies has to face a complex mixture of diverse scattering mechanisms. To characterize this complex scattering environment, nonparametric tomographic estimators are more robust to focusing artefacts but limited in resolution. Parametric tomographic estimators provide better vertical resolution but fail to adequately characterize continuously distributed volumetric scatterers such as forest canopies. To overcome these limitations, this paper addresses a new wavelet-based sparse estimation method for 3D imaging and characterization for underfoliage objects. The effectiveness of this new approach is demonstrated by using L-band Multi-Baseline PolInSAR Data over Dornstetten, Germany.

Synthetic Aperture Radar (SAR) Tomography (TomoSAR) provides access to the three-dimensional (3D) structure of illuminated media by jointly focusing multiple SAR acquisitions. TomoSAR imaging can be understood in simple terms by considering that multiple SAR flight lines, or orbits, allow forming a bi-dimensional synthetic aperture, resulting in the possibility to resolve the targets not only in the range-azimuth plane, but also in elevation. This simple principle brings along unprecedented possibilities, providing a way to investigate the illuminated media based on a direct observation of their vertical structure. The aim of this paper is to provide the readers with a brief tutorial on the use of TomoSAR imaging in the remote sensing of distributed media, by presenting basic imaging principles, applications, signal processing methods, and identifying challenges for future tomographic SAR systems.

A fundamental requirement for coherent processing of repeat pass SAR data stacks is to have precise knowledge of the relative position of each track. Indeed, sub-wavelength position errors give rise to residual phase screens among different passes, which hinder coherent applications. In this paper we describe an approach to estimate and remove phase screens by exploiting distributed targets, based on the concept of equivalent phase center. The proposed approach is demonstrated through numerical simulations and using campaign data. A cross-check of the results from simultaneous P- and L-Band acquisitions indicates that the dispersion of the retrieved flight trajectories is limited to a few millimeters. Preliminary results indicate that Capon-based tomographic imaging is more sensitive to phase errors, potentially resulting in artifacts that do not appear in Fourier-based approaches.

Information containing in the complex data is seldom considered by researchers when dealing with single synthetic aperture radar (SAR) image processing. In 2015, the statistical noncircularity, which indicates the distribution consistency between the real and imaginary parts, has been found to be surprisingly effective when analyzing the ultrahigh-resolution (UHR) millimeter-wave (MMW) SAR data set, particularly for man-made structures. However, the proposed parameter can only measure the overall noncircular level, which is inadequate to separate different noncircular behaviors. Moreover, its extraction method based on the complex generalized Gaussian distribution is very time consuming and thus makes it hard to be applied. Therefore, in this letter, we present a much simpler and more universal way to compute the noncircularity level and propose three specific parameters to specifically denote different noncircular behaviors. We also give two examples based on the real Chinese UHR MMW SAR (CUM-SAR) data set to show the abilities of the noncircularity level parameter and one example to show the preliminary ability of the specific noncircular parameters. Finally, the potentials and future application directions of these parameters are discussed. We believe that noncircularity parameters will benefit various research areas and promote the applications of UHR MMW SAR systems.

Synthetic aperture radar (SAR) data collected over a 2-D synthetic aperture can be processed to focus the illuminated scatterers in the 3-D space, using a number of signal processing techniques generally grouped under the name of SAR tomography (TomoSAR). A fundamental requirement for TomoSAR processing is to have precise knowledge of the platform position along the 2-D synthetic aperture. This requirement is not easily met in the case where the 2-D aperture is formed by collecting different flight lines (i.e., 1-D apertures) in a repeat-pass fashion, which is the typical case of airborne and spaceborne TomoSAR. Subwavelength platform position errors give rise to residual phase screens among different passes, which hinder coherent focusing in the 3-D space. In this paper, we propose a strategy for calibrating repeat-pass tomographic SAR data that allows us to accurately estimate and remove such residual phase screens in the absence of reference targets and prior information about terrain topography and even in the absence of any point- or surface-like target within the illuminated scene. The problem is tackled by observing that multiple flight lines provide enough information to jointly estimate platform and target positions, up to a roto-translation of the coordinate system used for representing the imaged scene. The employment of volumetric scatterers in the calibration process is enabled by the phase linking algorithm, which allows us to represent them as equivalent phase centers. The proposed approach is demonstrated through numerical simulations, in order to validate the results based on the exact knowledge of the simulated scatterers, and using real data from the ESA campaigns AlpTomoSAR, BioSAR 2008, and TropiSAR. A cross-check of the results from simultaneous P- and L-band acquisitions from the TropiSAR data set indicates that the dispersion of the retrieved flight trajectories is limited to a few millimeters.

This paper proposes to first use simulated Polarimetric Tomographic Synthetic Aperture Radar (PolTomSAR) data to spatially discriminate and characterize the ground signature under a vegetation layer. The ground signature estimated from the tomogram will then be analyzed to and the influence of the different scattering mechanisms on the entropy and SPAN simulated values at soil height, then simulated data are compared to measurements acquired by a ground based SAR system.

In this paper we present experimental results relative to the vertical structure of snow covered lake ice and sea ice, sensed with X-band radar system operated in a tomographic configuration. The available data are from a Ground-Based SAR campaign carried out over Prestvannet frozen lake and Kattfjord, both in Tromso, Norway. Direct imaging of the vertical structures of the snow and ice layers is achieved by focusing the signal from a 2D synthetic array in the 3D space. By making use of a priori information about the depth of snow and ice, the refractive index of snow and sea ice/lake ice is estimated from a single polarization tomographic measurement, and the results are in good agreement with previous experimental results. We have also shown that air bubbles in low salinity ice are the main contributors for the backscatter signal from ice.

This study aims to understand the effects of spatial resolution on the surface backscattering characteristics of polarimetric radar. Surface scattering models based on approximate methods are formulated by the roughness second-order statistics to obtain a closed-form expression for the radar scattering response. Most studies have been carried out based on the roughness parameters of the infinite surface. In this letter, we propose the roughness autocorrelation function of truncated surfaces for a more realistic description of the roughness parameters of high-resolution radar. The use of roughness parameters for a truncated surface in the scattering model is pertinent to explain the dependence of the backscattering coefficient on the spatial resolution. Simulation results indicate that the traditional computation of the surface backscattering based on the autocovariance function of an infinite surface leads to an underestimation of the backscattering signature of the high-resolution radar.

The rapid development of synthetic aperture radar (SAR) sensors results in the acquisition of substantial ultrahigh-resolution SAR images. In this paper, we, for the first time, present three scenes of single-polarization SAR images with decimeter resolution obtained by a millimeter-wave (MMW) Chinese airborne SAR system. An innovative framework based on the complex generalized Gaussian distribution (CGGD) model is proposed to extract land use information from them, and three CGGD parameters, including the shape parameter, the non-Gaussianity parameter, and the noncircularity parameter, are selected to identify different kinds of ground objects. It is shown that these parameters can reveal plentiful land surface information and will be extremely helpful for single-polarization SAR image interpretations. Moreover, a decision tree classifier is built to categorize these images into homogenous natural surfaces, vegetation textures, circular man-made targets, and noncircular man-made targets. Several interesting experiments are implemented, and their results well demonstrate the effectiveness of the new framework.

The following paper concerns preliminary results from a phenomenological analysis of vessel scattering carried out on Synthetic Aperture Radar Spotlight data. Time-frequency analysis tools have been applied to real data acquired from the German satellite Terrasar X, in order to detect and characterize the different scattering mechanisms contributing to the received signal. The impact and correction of data perturbations due to the motion of the ship are also discussed.

This paper deals with the energetic analysis of non-stationary scatterers in High-Resolution laboratory radar imaging. A method based on the well-known Multi-Dimensional Time-Frequency Analysis is proposed to extract marginal densities and highlights the frequency and angle (azimuth) signatures associated to a scatterer. Then, basic statistics are processed to characterize the frequency and/or aspect angle signatures. All in all, the efficiency of this technique is demonstrated, in an anechoic chamber experiment. On the one hand, statistics are linked to target characteristics, on the other hand the experiment shows the meaning of parameters and the efficiency of statistics can be discussed.

In this paper we present results relative to the 3D GBSAR surveys acquired in february 2013 on the Austrian Alps as a part of the ESA campaign AlpSAR. The GBSAR was operated at X- and Ku-Band with a bandwidth of 4 GHz and employing a 2D synthetic aperture, resulting in 3D resolution capabilities at a resolution of few centimeters. Images produced at two different sites reveal the presence of multiple layers within the snowpack. The strongest backscatter contributions have been observed to correspond to bottom layers, that dominate the ones from the snow/air interface and the near subsurface. GBSAR data are observed to provide sensitivity to the propagation velocity into the snowpack, as revealed by the apparent depth variation with respect to the incidence angle.

This paper proposes to use polarimetric tomographic synthetic aperture radar (PolTomSAR) data to spatially discriminate and characterize the ground signature under a vegetation layer. The influence of the different scattering mechanisms on the ground response is analyzed. The importance of the double-bounce is a limitation for the soil response characterization. A new algorithm is proposed to use the double bounce response to characterize the soil response and to estimate the soil moisture.

This paper presents a multilayer snowpack Electromagnetic Backscattering Model (EBM), based on Dense Media Radiative Transfer (DMRT). This model is capable of simulating the interaction of electromagnetic waves (EMW) at X-band and Ku-band frequencies with multilayer snowpack. The air-snow interface and snow-ground backscattering components are calculated using the Integral Equation Model (IEM), Fung et al. [1], whereas the volume backscattering component is calculated by the solution of Vector Radiative Transfer (VRT) equation at order 1. We have applied these models using measurement data from NoSREx report [2], which includes SnowScat data in X-band and Ku-band, TerraSAR-X acquisitions and snowpack stratigraphic profiles. The results of model simulations show consistency with the radar observations, and therefore allow the EBM to be used in various applications, such as data assimilation [3].

This paper presents an approach using data assimilation to take into account X-band Synthetic Aperture Radar (SAR) satellite observations in a detailed snowpack model. The SURFEX/Crocus snow model, developed by MeteoFrance, is used to simulate the detail stratigraphy of multilayer snow-pack from meteorological conditions. The Dense Media Radiative Transfer (DMRT) model allows the simulation of SAR backscattering coefficient using the physical parameters of snowpack (density and optical grain diameter of each layer). The development of an adjoint model of the DMRT and the implementation of three-dimensional variational (3D-Var) data assimilation algorithm enable us to reduce the discrepancy between the backscattering coefficients simulated using DMRT and measured using SAR satellite, through modifying the previously said physical parameters of snowpack. This approach provides the ability to constrain the detailed snowpack model SURFEX/Crocus using SAR observations and allows the retrieval of snowpack properties such as Snow Water Equivalent (SWE). Case study has been carried out using a time series of TerraSAR-X acquisitions on Argentière glacier (Chamonix Mont Blanc, France) in winter 2008-2009.

In this paper we present experimental results relative to the vertical structure of snow covered sea ice, sensed with X-band microwaves in a tomographic configuration. The available data are from a Ground-Based SAR campaign carried out by a team from the IETR, University of Rennes 1 in March 2013, over Kattfjord, Tromso, Norway, in collaboration with members of the EO lab at University of Tromso. Direct imaging of the vertical structures of the snow and sea ice layers is achieved by focusing the signal from a 2D synthetic array in the 3D space. The effect of propagation velocity in the focusing process is investigated and the tomograms are effectively corrected for this effect. The vertical structure of the medium reveals a strong response from sea ice. The effect of Brewster angle on the appearance of the tomograms is also investigated. Tomograms at two different polarizations are investigated and compared with dual-pol TerraSAR-X measurements.

In previous works, a general framework to exploit polarimetric diversity to optimize the results of persistent scatterers interferometry (PSI) was presented, but tested only with dual-pol data. In this paper, the performance of these algorithms is assessed using fully polarimetric data, acquired by the Radarsat-2 satellite over the urban area of Barcelona, Spain. In addition, two new highly efficient polarimetric optimization methods, mean intensity polarimetric optimization and joint diagonalization-based polarimetric optimization, are introduced and evaluated. Given the variety of dual-pol configurations provided by current polarimetric satellites, such as TerraSAR-X and Radarsat-2, and the upcoming launch of Sentinel-1, ALOS-2, and Radarsat Constellation Mission, a study has been also carried out to determine the best performing dual-pol configurations for polarimetric PSI. Subsidence maps of the area of study are computed for single-pol, dual-pol, and full-pol data, which show the increase in pixel density with valid deformation results as more polarimetric information is made available. In particular, for full-pol data we get an increase of up to 2.5 times more pixels for coherence-based PSI techniques (degraded resolution), and over four times more for amplitude-based approaches (full resolution), in comparison with single-pol data. Both higher density and quality of pixels yield better results in terms of coverage and accuracy.

In this paper, a coherent scattering model for natural media based on a Monte Carlo simulation of scattering from randomly distributed discrete spheroids is developed. The electromagnetic scattering problem is formulated with the electric field volume integral equation and solved by means of the method of moments with electrostatic basis functions. The model simulates the fields scattered by the medium for each of the realization of spheroid configurations generated using Metropolis shuffling method. The scattering model is then deployed to simulate polarimetric multi-baseline synthetic aperture radar (SAR) data. These data are afterwards processed to reconstruct the vertical profile of the polarimetric reflectivity density. The reflectivity density statistics are acquired via a Monte Carlo simulation over a large number of realizations. Using a P-band tomographic radar configuration, we first analyze the vertical profile of the polarimetric reflectivity density. Then we investigate the influence of spheroid concentration as well as the impact of electromagnetic coupling between scatterers on the reflectivity density.

This paper addresses the estimation of tropical forest parameters using polarimetric and tomographic SAR data. A new methodology based on an MB-Generalization of the Random-Volume-over-Ground (RVoG) model is introduced. This methodology consists in estimating forest height, its underlying ground topography and canopy vertical structure, after separation of the ground and volume contributions. Ground and volume separation is made using the Sum of Kronecker Product (SKP) decomposition method. Furthermore, a physical interpretation of the SKP decomposition solutions is provided. The proposed techniques are applied to P-Band MB-PolInSAR data acquired during the TropiSAR campaign over the test site of Paracou in French Guiana.

This paper proposes and compares different spectral estimation techniques to perform polarimetric 3-D imaging using SAR tomography (POLTOM). Parametric approaches based on the Random Volume over Ground model as well as non parametric techniques are proposed to derive 3-D full second-order polarimetric representations. Their theoretical performance is evaluated over simulated data sets and they are applied to the characterization of forested environments at L and P bands.

This paper proposes an algorithm for ship detection in complex scenes using multi-dimensional coherent Time-Frequency (TF) techniques and dual-polarization SAR data. The PolSAR multi-dimensional information is analysed by means of a linear TF decomposition approach which permits to describe the ship and background area polarimetric behaviour for different azimuth angles of observation and frequencies of illumination. A statistic descriptor related to the signal polarimetric coherence in the TF domain, is used for ship detection in different backgrounds, including the environments with presence of strong ghost ambiguities or small natural islands. Using polarimetric RADARSAT-2 data, experimental results demonstrate the efficiency of this method.

This paper addresses under-foliage target detection using diverse detection schemes. Compared with classical detection schemes as GLRT and SSF-based detection, isolated scatterer selection is potential to eliminate volume effects and detect under-foliage targets. The full-rank polarimetric spectral estimators are also applied for under-foliage detection. The effectiveness of diverse detection schemes are demonstrated by using L-band Multi-Baseline PolInSAR Data over Dornstetten, Germany.

In this paper we present experimental results relative to the vertical structure of a 60 cm snow-pack as sensed with X- and Ku-Band microwaves. The available data are from a Ground Based (GB) SAR campaign carried out by the University of Rennes I in December 2010 at Col de Porte, in the French Alps, in collaboration with Meteo-France. The data have been acquired by moving a VNA along two orthogonal directions, so as to obtain a two dimensional synthetic array. This allowed to focus the signal in the three dimensional space, thus providing a direct imaging of the vertical structure of the snow-pack at a resolution of few centimeters. Results revealed the presence of strong backscattering contributions from beneath the snow layer, that appear to be linked to the presence of an ice layer.

In this paper, an attempt of Alpine glacier flow modeling is performed based on a series of high resolution TerraSAR-X SAR images and a Digital Elevation Model. First, a glacier flow model is established according to the fluid mechanics theory in a simplified framework. Second, the displacement field over the glacier obtained from the sub-pixel image correlation of a series of TerraSAR-X SAR images is used to refine the model obtained previously. The comparison between the data observation and the model prediction allows for the validation of the established model. According to the obtained results, despite the simplifications made in the modeling, the established glacier flow model can provide general satisfactory results. Further investigation and improvement of this glacier flow model seem promising.

The classification of high-resolution and fully polarimetric SAR/ISAR data has gained a lot of attention in remote sensing and surveillance problems and is addressed by decomposing the radar target Sinclair matrix. In this paper, the Sinclair matrix has been projected onto the circular polarization basis and is decomposed into five parameters that are invariant to the relative phase$\Phi$, the Faraday rotation$\Omega$, and the target orientation$\Psi$without any information loss. The physical interpretation of these parameters, useful for target classification studies, is found in the wave-particle nature of radar scattering phenomenon given the circular polarization of elemental packets of energy. The proposed deterministic target decomposition is based on the left-orthogonal special unitary SU(2) basis, decomposing the signal backscattered by point targets, represented by the target vector, via six special unitary SU(4) rotation matrices, and by providing full resolution and lossless analysis. Comparisons between the proposed deterministic target decomposition and the Cameron, Kennaugh, Krogager, and Touzi decompositions are also pointed out. Generally, the proposed decomposition provides simpler interpretation, faster parameter extraction, and better generalization properties for the analysis of nonreciprocal or random targets. Several polarimetric SAR/ISAR data sets of UWB data, airborne fully polarimetric EMISAR data, and spaceborne RADARSAT2 are used for illustrating the effectiveness and the usefulness of this decomposition for the classification of point targets. Results are very promising for application use in the next generation of high-resolution spaceborne and airborne Pol-SAR and Pol-ISAR systems.

The contribution of Polarimetric Synthetic Aperture Radar (PolSAR) images compared with the single-channel SAR in terms of temporal scene characterization has been found and described to add valuable information in the literature. Recently, a new PolSAR tracking algorithm has been proposed for glacier surface velocity monitoring. The proposed polarimetric tracking method applies Mutual Information (MI) to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximum if the images are geometrically aligned. In this paper, its implementation of interest will be investigated.

This paper presents new principles and techniques to perform High Resolution (HR) 3-D imaging of volumetric environments using Polarimetric SAR Tomography (POLTOMSAR) and Multi-Baseline Polarimetric and Interferometric SAR (MB-POL-inSAR) data. Unlike classical polarimetric spectral estimation approaches which consider polarization as way to improve the discrimination between vertically aligned scatterers, this paper provides full rank techniques which permit to estimate 3-D coherency matrices that can be characterized using classical polarimetric processing algorithms.

This paper proposes a way of improvement of the ML texture tracking method using bootstrap sampling. A quality factor is introduced to measure the accuracy of estimation. It is based on both statistics and image processing. The bootstrap sampling uses the initial information for generating additional samples in order to enhance the available information. Some results on particular areas of a glacier are presented.

This paper addresses the characterization of tropical forest structure by estimating their heights, underlying ground topography, vertical structure function and ground-to-volume ratio. The hybrid tomographic estimator can accurately estimate the tree top heights and the underlying topography. In order to separate the ground and volume contributions, model-based two-component fitting techniques are proposed to reconstruct the vertical structure function of forests. The proposed techniques are applied to P-Band Multi-baseline PolInSAR data acquired during the TropiSAR campaign over the test site of Paracou in French Guiana.

The advantages of the new generation of radar systems with high resolution image, short revisit time provide the possibility of characterization and monitoring the evolution of the cryoshpere. In this paper, we propose an adaptation of the multilayer snow backscattering model based on radiative transfer theory in order to estimate the total backscattering coefficient of high frequency (X-band) electromagnetic wave on snowcover area. Next, from the physical model, we develop the adjoint operator and implement a variational assimilation scheme in order to constrain the snow stratigraphy profiles calculated by CROCUS, a snow metamorphism model used by MeteoFrance. Some tests are carried out with TerraSAR-X image data. The results show that the snow stratigraphy profiles obtained after the data assimilation process have good agreement with the measured profiles, and therefore show the high potential of this method in constraining the snowpack profiles of CROCUS.

The objective of this paper is to make a review of the current status of the PolSARpro v5.0 Software (Polarimetric SAR Data Processing and Educational Toolbox), developed under contract to ESA by I.E.T.R at the University of Rennes 1. The objective of this current project is to provide Educational Software that offers a tool for self-education in the field of Polarimetric SAR data analysis at University level and a comprehensive suite of functions for the scientific exploitation of fully and partially polarimetric multi-data sets and the development of applications for such data. The PolSARpro v5.0 Software establishes a foundation for the exploitation of Polarimetric techniques for scientific developments and stimulates research and applications developments using PolSAR and PolInSAR data.

This paper proposes a new method for a measure of coherent similarity between temporal multichannel synthetic aperture radar (SAR) images and its implementation to change detection application. The method is based on mutual information (MI) from information theory. The MI measures the amount of information in common between coherent temporal multichannel SAR acquisitions. In order to develop an algorithm for all kinds of SAR images, such as interferometric SAR, polarimetric-interferometric SAR (PolInSAR), and partial PolInSAR, first, the joint density function of temporal multichannel images based on their second-order statistics has been derived. Then, the derived joint density function is used to calculate an analytical expression for the MI between temporal images, which is assumed to be maximal if the temporal images are identical. Although, in this paper, a new coherent similarity measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can equally well be implemented into any kind of multivariate remote sensing data, such as multispectral optical and interferometric images after small continuation. This derived quantity has been implemented for change detection application whose aim is to characterize the temporal behavior of the acquisitions. A comparison between the proposed and the other well-known change detection methods by means of scene characterization is shown, describing the advantages due to the fact that the proposed change detector involves almost every facet of applied change detection.

This paper addresses the imaging of objects located under a forest cover using polarimetric synthetic aperture radar tomography (POLTOMSAR) at L-band. High-resolution spectral estimators, able to accurately discriminate multiple scattering centers in the vertical direction, are used to separate the response of objects and vehicles embedded in a volumetric background. A new polarimetric spectral analysis technique is introduced and is shown to improve the estimation accuracy of the vertical position of both artificial scatterers and natural environments. This approach provides optimal polarimetric features that may be used to further characterize the objects under analysis. The effectiveness of this novel technique for POLTOMSAR is demonstrated using fully polarimetric L-band airborne data sets acquired by the German Aerospace Center (DLR)'s E-SAR system over the test site in Dornstetten, Germany.

The target coherency or covariance matrices are the main operators useful for characterizing the polarization transformation properties of radar target by modeling the depolarization effect. In this paper, a novel decomposition of the target coherency matrix is proposed, that is sufficient for representing the physical characteristics of the observed medium in term of a minimum set of orientation invariant parameters. The Einstein's photon circular polarization basis is used for obtaining an orientation invariant physical interpretation of the proposed parameter set both for deterministic and random target. A generalized unsupervised classification scheme is also proposed for underlining the effectiveness of the proposed decomposition theorem for classifying random reciprocal target into 75 physically meaningful clusters. The application of the proposed decomposition theorem and classification algorithm is useful for developing of novel Remote Sensing products and Data Mining softwares for monitoring the surfaces of the Earth and the Moon.

This paper analyzes the effects of speckle filtering on polarimetric SAR decomposition and classification. We compared the results of the refined Lee, ID AN and Non-Local Polarimetric filters, and discussed their influence on the Cloude-Pottier decomposition and the Wishart H/α classification. ALOS/PALSAR and RadarSat-2 polarimetric SAR data are used for illustration.

This paper concerns the definition of efficient configurations of acquisition for improving the features of tomographic imaging using a constellation of SAR sensors. MIMO SAR measurements done in monostatic and bistatic configurations permit to enhance the spectral diversity of the acquisition in the vertical direction, yielding higher performance in terms of ambiguity height and resolution. Different cases are investigated, and in particular a configuration with tracks that would reveal ambiguous for systems operating in monostatic mode only, and another one consisting of sparse groups of acquisitions. The validity of the proposed approach is demonstrated using data sets acquired by a versatile multi channel Ground-Based SAR system, operating in alternating emit-receive mode, over coherent scatterers and distributed volumetric media.

This paper presents principles and techniques to perform tomographic imaging of volumetric natural environments using Multi-Baseline Polarimetric and Interferometric SAR (MB POL-inSAR) data. The objective of this work is to provide robust techniques to reconstruct the 3-D structure of media and to estimate some of their physical parameters. The pro posed approaches are based on results obtained in [1] on the robust estimation of MB-POL-inSAR quantities, on the POLINSAR model presented in [2] and on the tomographic techniques introduced in [3] and [4].

We discuss in the paper the use of the Riemannian mean given by the differential geometric tools. This geometric mean is used in this paper for computing the centers of class in the polarimetric H/α unsupervised classification process. We can show that the centers of class will remain more stable during the iteration process, leading to a different interpretation of the H/α/A classification. This technique can be applied both on classical SCM and on Fixed Point covariance matrices. Used jointly with the Fixed Point CM estimate, this technique can give nice results when dealing with high resolution and highly textured polarimetric SAR images classification.

This paper addresses the characterization of tropical forests by estimating their heights and the underlying ground topography. A novel hybrid spectral approach is proposed and applied to P-Band Multi-baseline PolInSAR data acquired during the TropiSAR campaign over the test site of Paracou in French Guiana. Experimental results demonstrates that tropical forest heights and the underlying ground topography can be accurately estimated by this method, compared with those derived by single-baseline PolInSAR parameter retrieval techniques. The estimated quantities are validated against LiDAR measurements during TropiSAR campaign.

This paper presents a data mining approach for describing Satellite Image Time Series (SITS) spatially and temporally. It relies on pixel-based evolution and sub-evolution extraction. These evolutions, namely the {frequent grouped sequential patterns}, are required to cover a minimum surface and to affect pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. In this paper, a specific application to fully polarimetric SAR image time series is presented. Experiments performed on a RADARSAT-2 SITS covering the Chamonix Mont Blanc test-site are used to illustrate the proposed approach.

In this letter, a simulation procedure for airborne stripmap synthetic aperture radar with zero squint angle is proposed. The procedure is based on the idea of inverse processing and 1-D summation. It utilizes narrow bandwidth approximation and is more efficient compared to a time-domain simulator. Moreover, it allows for the simulation of raw data for higher aperture angle configurations compared to existing approaches. The effectiveness of the approach is demonstrated by means of examples for point scatterers.

This paper introduces new polarimetric algorithms for generating 3-D images and estimating scattering mechanisms from polarimetric multibaseline (MB) interferometric synthetic aperture radar (SAR) measurements. First, an MB interferometric SAR signal model is generalized to the fully polarimetric configuration, establishing the notion of polarimetric reflectivity. Subsequently, polarimetric beamforming, Capon, and MUSIC methods that determine optimal polarization combinations for height estimation are developed. These new techniques allow for extracting the height of reflectors, the associated scattering mechanisms, and the polarimetric (pseudo)reflectivities. By means of polarimetric dual-baseline interferometric SAR observations of an urban environment, the performance of the conceived algorithms is examined in detail. Producing 3-D images of a building layover, the quality of the approaches is compared in terms of refined resolution and lowered side lobes. Furthermore, the scattering processes occurring in urban scenes are investigated thoroughly by analyzing the optimal reflection types. The algorithms are validated using dual-baseline polarimetric SAR interferometric data at L-band acquired by German Aerospace Center's experimental SAR system over Dresden city.

Modern SAR systems have high resolution which leads the backscattering clutter to be non-Gaussian. In order to properly classify images from these systems, a non-Gaussian noise model is considered: the SIRV model. A statistical test of equality of covariance matrices is used to classify pixels, taking into account the critical region of the test which rejects the likeliness of a covariance matrix to any of the class centers. This test is applied on experimental data obtained with the ONERA RAMSES system in X-band. The results show a good separation between natural and man-made areas of the image.

This paper introduces a generalisation of the conventional Maximum Likelihood (ML) texture tracking algorithm in the context of highly heterogeneous PolSAR clutter. The statistical criterion is defined in both uncorrelated and correlated texture cases. Some results on simulated data are computed and an application on temperate glaciers velocity estimation is processed. Finally, some additional improvements are performed: an adaptative sliding windows is set and a basic Bayes inference for flow model constraint is added.

SAR tomography is the extension of conventional two dimensional SAR imaging principle to three dimensions. In order to improve the vertical resolution with respect to classical Fourier-based methods, high resolution approaches are used in this paper to perform SAR tomography. Both nonparametric spectral estimators, like beamforming and Capon and parametric ones, like MUSIC, maximum likelihood, are applied to real data sets and compared in terms of scatterer location accuracy and resolution. This paper addresses the discrimination of coherent scatterers presented in the natural environment and a joint approach of estimation and detection is proposed.

This paper concerns forest parameter retrieval from polarimetric interferometric synthetic aperture radar (PolInSAR) data considering two layers, one for the ground under the vegetation and one for the volumetric canopy. A model is designed to combine a physical model-based polarimetric decomposition with the random-volume-over-ground (RVoG) PolInSAR parameter inversion approach. The combination of a polarimetric scattering media model with a PolInSAR RVoG vertical structure model provides the possibility to separate the ground and the volume coherency matrices based on polarimetric signatures and interferometric coherence diversity. The proposed polarimetric decomposition characterizes volumetric media by the degree of polarization orientation randomness and by the particle scattering anisotropy. Using the full model enhances the estimation of the vertical forest structure parameters by enabling us to estimate the ground-to-volume ratio, the temporal decorrelation, and the differential extinction. For forest vegetation observed at L-band, this model accounts for the ground topography, forest and canopy layer heights, wave attenuation in the canopy, tree morphology in the form of the angular distribution and the effective shapes of the branches, and the contributions from the ground level consisting of surface scattering and double-bounce ground-trunk interactions, as well as volumetric understory scattering. The parameter estimation performance is evaluated on real airborne L-band SAR data of the Traunstein test site, acquired by the German Aerospace Center (DLR)'s E-SAR sensor in 2003, in both single- and multibaseline configurations. The retrieved forest height is compared with the ground-truth measurements, revealing, for the given test site, an average root-mean-square error (rmse) of about 5 m in the repeat-pass configuration. This implies an improvement in rmse by over 2 m in comparison to the pure coherence-based RVoG PolInSAR parameter inversion.

This paper deals with the analysis of non-stationary scatterers in SAR images. Indeed, SAR imaging makes the assumptions that the scatterers are isotropic and white in the emitted frequency band. However, new SAR applications use a large bandwidth and a strong angular excursion. These assumptions become obsolete and the behavior of scatterers becomes non-stationary. The basic tool to study non-stationary signals is the time-frequency analysis. Recent studies based on multidimensional Time-Frequency Analysis describing the angular and frequency behavior of scatterers has highlighted anisotropic and dispersive behavior of bright points. This paper generalizes the hyperimage concept to study scatterers. Multidimensional Time-Frequency distributions are tested on simulations, then they are applied to very high resolution SAR images and show some scatterers are anisotropic and dispersive.

In radar imaging, the assumption is made that scatterers are white in the emitted frequency band and isotropic for all direction of observation. Nevertheless, new capacities in radar imaging, using a wideband and a large angular excursion, make these hypotheses not valid. Time-frequency analysis highlight this point of view and show some scatterers are anisotropic and/or dispersive. This information source can be completed by radar polarimetry. This paper suggests a supervised classification of scatterers using neural networks based on polarimetric time-frequency signatures. This method is applied here on anechoic chamber data, however can be generalized to SAR or circular SAR imaging.

Due to their large scale of observation and their relatively high revisiting frequency, spaceborne SAR systems offer interesting possibilities for the systematic monitoring of urban areas. Several techniques have been developed to analyze urban areas from single-polarization spaceborne SAR data, based on the statistical properties of the reflectivity of such complex media and its spatial variations (texture). The reduced resolution of the data, compared to the airborne SAR case, is a particularly limiting factor. Polarization diversity offers an interesting and powerful alternative mean to detect and characterize urban areas. In this paper, we propose to use po-larimetric SAR acquired by the ALOS sensor at L band, to monitor urban areas. The proposed technique uses three complementary approaches to discriminate urban structures using detectors adapted to the complex polarimetric features of this medium, to isolate specific coherent responses from a Time-Frequency analysis of the coherent SAR signal, and finally to characterize built-up areas from the coherence properties of their Polarimetric and Interferometric SAR (POL-inSAR) response.

This paper presents principles and robust techniques to estimate physical parameters of natural environments using Multi-Baseline Polarimetric and Interferometric SAR (MB-POL-inSAR) data. The first part of this paper concerns the abstract topic of MB-POL-inSAR coherence optimization The second part is dedicated to the general estimation of the coherence line model parameters [1]. It is demonstrated that the line parameters can be estimated in an analytical and robust way by using the whole available POL-inSAR information.

Diverse spectral estimations methods, i.e. MUSIC, Maximum Likelihood (ML), Weighted Subspace fitting (WSF), are proposed and applied to the estimation of building height dense urban environments and to the retrieval of scatterers' physical properties. Compared to other estimators, the polarimetric WSF estimator is optimally model adaptive and results in reduced sidelobes induced by irregularly sampled baselines.

The advances in Polarimetric SAR Interferometry (PolInSAR) techniques provide a promising way to recover ground characteristics such as sub-canopy soil moisture and roughness using SAR data. Spectral analysis techniques have been applied to extract the vegetation and building parameters. Yamada et al proposed the ESPRIT algorithm to estimate vegetation height; Sauer et al apply the spectral analysis techniques to estimate building heights and extract physical properties from Multi-baseline (MB) PolinSAR data. In these applications, the parameters are mainly estimated from the phase information or phase center, but the validity of the sub-canopy soil backscattering or reflectivity estimation from polarimetric spectral analysis technique is not investigated. In this paper, the ground scattering center is first located by po-larimetric MUSIC algorithm and then the ground reflectivity is recovered using a polarimetric least-square method. The validity of the polarimetric spectral analysis technique for the sub-canopy ground reflectivity estimation is demonstrated using simulated and real SAR data.

A simple vegetation model for polarimetric covariance and coherency matrix elements is presented. The model aims to represent vegetation characteristics which are observable by radar polarimetry, including the average particle scattering anisotropy, the main orientation of the volume, the degree of orientation randomness in the volume, and the terrain slopes. The goal of this approach is to quantify these parameters and to enable their estimation in a remote sensing parameter inversion framework. The retrieval of parameters related to effective particle shapes in the polarization plane and the orientation distribution characteristics is evaluated on real SAR data acquired by DLR's E-SAR system at L-band.

This paper concerns forest parameter retrieval from multi - temporal polarimetric interferometric SAR data. A two - component polarimetric interferometric model, designed for geophysical parameter retrieval, is presented for volumetric media over the ground. It is founded on a scattering model based polarimetric decomposition and the random volume over ground (RVoG) PolInSAR inversion technique. For forest vegetation observed at L-band, this model accounts for the ground topography, canopy layer and total tree heights, mean wave attenuation in the canopy, tree morphology in the form of orientation distribution and effective shapes of the branches, surface scattering contribution, and double - bounce ground-trunk interactions. A parameter retrieval framework is developed for repeat-pass acquisitions which aims to estimate and to compensate temporal decorrelation. The parameter estimation performance is evaluated on real airborne L-band SAR data in the repeat pass mode.

Due to their large scale of observation and their relatively high revisiting frequency, spaceborne SAR systems offer interesting possibilities for the systematic monitoring of land cover. Several techniques have been developed to analyze land cover areas from single-polarization spaceborne SAR data, based on the statistical properties of the reflectivity of such complex media and its spatial variations (texture). The reduced resolution of the data, compared to the airborne SAR case, is a particularly limiting factor. Polarization diversity offers an interesting and powerful alternative mean to characterize land cover areas. In this paper, we propose to use polarimetric SAR acquired by the ALOS sensor at L band, to monitor land cover of an African region.

The objective of this paper is to make a review of the current status of the PolSARpro v4.0 Software (Polarimetric SAR Data Processing and Educational Toolbox), developed under contract to ESA by a consortium comprising I.E.T.R at the University of Rennes 1, AELc, DLR-HR and Dr mark Williams from Adelaide. The objective of this current project is to provide Educational Software that offers a tool for self-education in the field of Polarimetric SAR data analysis at University level and a comprehensive suite of functions for the scientific exploitation of fully and partially polarimetric multi-data sets and the development of applications for such data. The PolSARpro v4.0 Software establishes a foundation for the exploitation of Polarimetric techniques for scientific developments and stimulates research and applications developments using PolSAR and PolInSAR data.

This letter generalizes a multibaseline interferometric synthetic aperture radar (InSAR) signal model to the polarimetric scenario. Based on this formulation, two high-performance spectral analysis techniques are adapted to process multibaseline Pol-InSAR observations. These new methods enhance the height estimation of scatterers by calculating optimal polarization combinations and allow the determination of their physical characteristics. Applying the proposed algorithms to urban environments, the building layover problem is analyzed by means of polarimetric dual-baseline InSAR measurements: the ground and building height are estimated. The techniques are validated using dual-baseline Pol-InSAR data acquired by DLR's Experimental SAR (E-SAR) system over Dresden city.

This letter describes efficient synthetic aperture radar raw data generation in the wavenumber domain using inverse processing. An exact form based on an inverse Omega-k algorithm and an approximate form based on an inverse Chirp Scaling algorithm are described, and examples are presented for a point scatterer. A methodology to incorporate the anisotropic behavior of an urban scene in a reflectivity map is also presented and demonstrated by means of a subaperture analysis.

This paper presents a method to characterize snow cover in mountainous regions using dual-polarization C-band synthetic aperture radar (SAR) data. It is demonstrated that an accurate modeling of the liquid water distribution inside the snowpack, using a multilayer meteorological snow model, is required to characterize snow with precision. A multilayer-snow electromagnetic (EM) backscattering model is developed based on the vector radiative transfer, the strong fluctuation theory, and physical parameters supplied by the meteorological model. However, the limited resolution of the meteorological snow model is insufficient for predicting a refined EM backscattering at a massif scale. An adequate spatial reorganization of these snow profiles, based on a comparison between simulated and measured dual-polarization SAR data, leads to a better estimation of some snowpack parameters. In particular, the monitoring of snow liquid water content is presented improving the capacity of wet snow mapping as compared to a classical SAR-based method. This methodology shows good capacities both for qualitative and quantitative snow assessments, opening the way for a new operational method.

The aim of this paper is to analyze the feasibility of soil moisture retrieval over rough periodic surfaces. For this kind of fields, the Bragg phenomenon effect appears and the inversion algorithms are shown to be no more valid. Using a time-frequency approach and a new random periodic surface scattering model, a polarimetric analysis allows to show that the use of alpha1polarimetric parameter is important for the inversion since it is quasi insensible to this phenomenon. Thus, the alpha1inversion method is proposed that provides some encouraging results.

This paper introduces statistical tools for the analysis of POL-inSAR data acquired over natural environments. Maximum likelihood procedures are provided to test particular structures of POL-inSAR data presentations an to estimate relevant parameters. These particular configurations concern the stationarity of the separate POLSAR information between, the equality of the optimal POL-inSAR projection vectors and the presence of reflection symmetry.

This paper focuses on the physical understanding of the polarimetric interferometric SAR (PolInSAR) coherence, and on the accurate utilization of this coherence for vegetation parameter inversion. A polarimetric interferometric vegetation model presented here provides the possibility to estimate parameters related to ground topography, vegetation, and surface scattering. In particular, one can estimate such vegetation characteristics as the main orientation, the degree of orientation randomness and the effective shape of the particles, together with structural parameters like the ground height and the depths of vegetation layers. The polarimetric model is based on the Freeman's three component decomposition, which is extended to consider vegetation orientation. To enhance polarimetry, a complimentary interferometric coherence model is presented. The model and the parameter inversion method are tested on accurate electromagnetic simulations of real forests. Parameter inversion performance is evaluated for single-baseline and multibaseline data.

Statistical properties of rough surfaces can be affected by the spatial resolution of the radar sensor. This study aims to understand effects of the spatial resolution on the statistical description of surface roughness properties and on the surface scattering characteristics of polarimetric radar signal. A new expression for the surface autocovariance function and its corresponding roughness spectrum is proposed in order to characterize surface scattering. Results represent increases in the backscattering coefficients in accordance with decreases in the resolution cell size. Conventional continuous spectrum in the theoretical scattering model could underestimate the radar response in case of the surface defined by high correlation length.

The objective of this paper is to make a review of the current status of the PolSARpro v3.3 Software (Polarimetric SAR Data Processing and Educational Toolbox), developed under contract to ESA by a consortium comprising I.E.T.R at the University of Rennes 1, AELc, DLR-HR and Dr mark Williams from Adelaide. The objective of this current project is to provide Educational Software that offers a tool for selfeducation in the field of Polarimetric SAR data analysis at University level and a comprehensive suite of functions for the scientific exploitation of fully and partially polarimetric multi-data sets and the development of applications for such data. The PolSARpro v3.3 Software establishes a foundation for the exploitation of Polarimetric techniques for scientific developments and stimulates research and applications developments using PolSAR and PolInSAR data.

Modern space-borne SAR sensors, like ALOS-PALSAR, TerraSAR-X and Radarsat-2 all provide at least a "partial polarimetric mode", acquiring only 2 of the 4 elements of the Sinclair matrix, like for example HH and HV or VV and VH. In addition, it has been demonstrated that with certain so-called "compact PolSAR" single-transmit dual-receive techniques one can obtain an estimation of the fully polarimetric information. Such systems are attractive in terms of reduction of pulse repetition frequency, data rate, and complexity and are currently very popular. However, they do not acquire complete information pertaining to the full polarisation state of the target and, as a consequence also coherence optimisation suffers from the reduced configuration space. In this paper, the potential of the different partial polarimetric setups for coherence optimisation is evaluated both theoretically and experimentally and compared to the capabilities of a fully polarimetric system. It will be analysed to which extent partial polarimetric system can improve the derivation of interferometric information from partly decorrelated surfaces, in particular of vegetated or even forested areas. Special attention is paid to the constrained coherence optimisation of multi-baseline setups, important for modern DInSAR techniques like PS analysis and continuous DInSAR monitoring in general. All experimental analyses will be performed using fully polarimetric multi-baseline data sets. For proper comparison, partial polarimetric information is derived from these by matrix transformations according to the respective transmit / receive configuration.

This paper generalizes a multibaseline interferometric SAR signal model to the polarimetric configuration. Based on this formulation, two high-performance array signal processing techniques are adapted to analyze multibaseline POL-InSAR observations. These new methods enhance the height estimation of scatterers by calculating optimal polarization combinations and allow the determination of their physical characteristics. Applying the algorithms to urban environments, the building layover problem is resolved by means of polarimetric dual-baseline InSAR measurements: Up to two components within one azimuth-range resolution cell are separated. The techniques are tested using dual-baseline Pol-InSAR data acquired by DLR's E-SAR system over Dresden city.

This letter deals with the analysis of moving and nonstationary objects in already focused synthetic aperture radar (SAR) images. A method based on the matching pursuit (MP) algorithm is proposed to decompose the SAR signal into a set of atoms. A model of a moving object response with frequency- and angle-dependent reflectivity is introduced to design the MP atoms. Each selected atom is associated to a scatterer of the scene and is parameterized by relevant physical descriptors, leading to a multidimensional model of the object. The efficiency of this technique is demonstrated in terms of SAR response refocusing and physical parameter map derivation, using a polarimetric SAR image of a moving object lying in a natural background.

This letter analyzes different approaches for polarimetric optimization of multibaseline (MB) interferometric coherences. Two general methods are developed to simultaneously optimize coherences for more than two data sets. The first method provides every data set with a distinct dominant scattering mechanism (SM). The second optimization method is constrained to use equal SMs at all data sets. As the experimental results indicate, MB coherence optimization does improve the accuracy in the estimation of dominant SMs and the associated interferometric phases. Both methods are evaluated on real data acquired by the German Aerospace Agency (DLR)'s enhanced synthetic aperture radar sensor (ESAR) at L-band.

This paper deals with the validation of a previously developed texture model for SAR data as well as its associated parameter estimation algorithm. The mentioned model is named the Anisotropic Gaussian Kernel (AGK) model and allows the description of the possibly nonstationary and anisotropic behaviour of texture on heterogeneous areas of SAR images. The parameter estimation performance is evaluated over simulated data. We also investigate about the validity of our model over experimental data, by means of dissimilarity measures.

Synthetic Aperture Radar (SAR) images built from received signals are high-resolution maps of the spatial distribution of the reflectivity function of targets. Conventional radar imaging assumes that all the scatterers are considered as bright points (isotropic for all observation angles and white in the frequency band) [1]. Recent studies based on multidimensional Time-Frequency Analysis describe the angular and frequency behavior of scatterers and show that they are anisotropic and dispersive [2]. Another useful information source in radar imaging is the polarimetry Studies based on multidimensional wavelet and coherent decompositions allow to represent the angular and frequency polarimetric behavior and show the non-stationarity of this behavior. The aim is to characterize scatterers by time-frequency analysis and polarimetry.

This article presents a statistical approach for the study of PolSAR images using Time-Frequency (TF) correlation properties. PolSAR information is analyzed using a linear time- frequency (TF) decomposition which permits to describe a scene polarimetric behavior for different azimuth angles of observation and frequencies of illumination. A TF signal model is proposed and studied using two statistical descriptors related to the signal stationary aspect and coherence in the time-frequency domain. These indicators are shown to provide complementary information for an enhanced description of the scene.

This paper analyzes a POLInSAR coherence model with respect to polarization diversity. The coherence constituents are identified and examined. Extending POLInSAR to multiple baselines, two general multibaseline coherence optimization methods are introduced. The coherence model is utilized to discuss the advantages and applications of these newly developed multibaseline coherence optimization methods.

This paper generalizes a multibaseline interferometric SAR signal model taking polarization diversity into account. Based on this formulation, two high-performance spectral analysis techniques are extended to the multibaseline POL-InSAR configuration. These new algorithms enhance the height estimation of scatterers by calculating optimal polarization combinations and allow to determine their physical characteristics. Applying the methods to urban scenes, experimental results show the retrieval of building height and polarimetric properties by means of single-baseline polarimetric datasets. Dual-baseline observations permit the solution of the layover problem by separating two contributions within one resolution cell. The algorithms are tested using multibaseline Pol-InSAR data acquired by DLR's E-SAR system over Dresden city.

Estimating the number of backscattering sources is an important issue in analyzing multibaseline interferometric SAR data. This paper extends model order selection algorithms to process polarimetric multibaseline InSAR observations. These methods are applied to urban environments using fully polarimetiric dual-baseline InSAR data of Dresden city acquired by DLR's E-SAR system. Experimental results for single polarization and polarimetric set-ups are presented and discussed.

This paper deals with spatial analysis of texture in synthetic aperture radar (SAR) images. A new parametric model for local two-point statistics of the image is introduced, in order to characterize the spatially nonstationary and anisotropic behavior of the image. The texture is first modeled by a nonstationary Gaussian process resulting from the convolution of a Gaussian white noise with a field of anisotropic Gaussian kernel with spatially varying parameters. Hence, under the hypothesis of locally stationary signal, the analytic expression of the local autocovariance is derived. It is then explained how to simulate nonstationary K-distributed random fields by combining the new model with an already existing simulation method. A method for parameter estimation is then introduced. This method, based on the statistical product model, first corrects the speckle contribution to the local autocovariance and estimates the parameters of the model by analyzing the shape of the autocovariance. The algorithm is then evaluated over simulated and experimental data. Stationary simulations permit to show that, for a sufficient sample size, the estimator is unbiased. A test over a nonstationary simulation proves the ability of the algorithm to capture the spatial fluctuations of the texture. Finally, the method is applied to the experimental SAR data, and it is shown that a large amount of spatial information may be retrieved from the data.

In this paper, the analysis of spatially nonstationary texture from polarimetric SAR data is studied. A previously introduced model named Anisotropic Gaussian Kernel (AGK) was shown to be a pertinent descriptor of local orientation and allowed a simple representation of the complex spatial structure in SAR images. Here, two methods for the estimation of the model parameters are proposed. The first one is an enhancement of the previously developed algorithm and the second one is a new approach based on the Gradient Structure Tensor (GST) operator. These two methods are employed to analyse texture in PolSAR intensity channels.

This letter proposes a new model for the second-order statistics of spatial texture in synthetic aperture radar images. The autocovariance function is locally approximated by a two-dimensional anisotropic Gaussian kernel (AGK) to characterize texture by its local orientation and anisotropy. The estimation of texture parameters at a given scale is based on the gradient structure tensor operator and does not require the explicit computation of the autocovariance. Finally, a new filter called AGK minimum mean square error (MMSE) that takes into account this spatial information is introduced and compared with the refined MMSE filter. The proposed filter has better performance in terms of texture preservation and structure enhancement

One of the main limitations of electromagnetic incoherent vegetation models based on the radiative transfer theory concerns their inability to take into account the coherent effect occurring in a double-bounce scattering mechanism. This is particularly important for low-frequency synthetic aperture radar applications over forested areas, where large branch and trunk contributions may be preponderant. In this letter, an easily computable solution based on the reciprocity theorem is proposed. The coherent effect contribution is obtained directly from the radiative transfer incoherent solution through a straightforward correction algorithm. Compared to the classical vector radiative transfer first-order solution, this method provides the exact cross-polarized contribution to the radar cross section or to the polarimetric parameters computation. The theoretical developments are illustrated using electromagnetic full-wave and approximate scattering models.

This letter proposes an algorithm to improve the range resolution in airborne synthetic aperture radar (SAR) data by coherently combining an interferometric image pair, i.e., two images acquired with slightly different viewing angles. This algorithm is based on the wavenumber shift principle. In contrast to other methods, developed for application to spaceborne SAR data, the proposed algorithm takes the nonlinear effects due the strong variations in incidence angle in airborne SAR data into account. The proposed method is applied to SAR data of German Aerospace Center (DLR)'s E-SAR sensor. Quantitative verification results are obtained by measuring the resolution of several corner reflectors placed in the area under study, as well as the resolution of speckle of different areas. It is demonstrated that a resolution improvement of almost a factor of two can be achieved by incorporating a second interferometric image, which can be acquired easily with an airborne sensor.

Fully polarimetric synthetic aperture radar (SAR) data analysis has found wide application for terrain classification, land-use, soil moisture, and ground cover classification. Critical to all analyses and applications is accurate calibration of the relative amplitudes of and phases between the various polarimetric channels. Here we propose an a posteriori method imposing only the weakest of constraints, scattering reciprocity, on the polarimetric data. Calibration parameters are self-consistently estimated from full 4/spl times/4 polarimetric covariance matrices. Whilst the complete set of calibration parameters is underdetermined, we give several reasonable heuristic methods to provide a complete calibration. Stronger constraints reduce the number of independent parameters and provide an overdetermined set of equations but at a cost - the loss of polarimetric fidelity when the underlying assumptions are violated. Without recourse to in situ calibration targets, the extent of the polarimetric distortion that results from polarimetric calibration remains unknown. We apply our new method to simulated data, anechoic chamber data and polarimetric SAR imagery. We also present comparisons with alternate calibration methods and different approximate solutions of the new technique.

A new concept in polarimetric synthetic aperture radar (POLSAR) speckle filtering that preserves the dominant scattering mechanism of each pixel is proposed in this paper. The basic principle is to select pixels of the same scattering characteristics to be included in the filtering process. To achieve this, the algorithm first applies the Freeman and Durden decomposition to separate pixels into three dominant scattering categories: surface, double bounce, and volume, and then unsupervised classification is applied. Speckle filtering is performed using the classification map as a mask. A single-look or multilook pixel centered in a 9 /spl times/ 9 window is filtered by including only pixels in the same and two neighboring classes from the same scattering category. This filter is effective in speckle reduction, while perfectly preserving strong point target signatures, and retains edges, linear, and curved features in the POLSAR data. The effect of speckle filtering on scattering characteristics, such as entropy, anisotropy, and alpha angle, will be discussed.

The aim of this paper is to present two novel polarimetric parameters, the eigenvalue relative difference (ERD) and the single bounce eigenvalue relative difference (SERD), to characterize natural media. These parameters are derived from the eigen-decomposition of the coherency matrix considering the reflection symmetry hypothesis. An analysis of these parameters is performed on multi-frequency polarimetric SAR data acquired on bare soils and forested areas.

This paper proposes a new model for the two-point statistics of spatial texture in SAR images. The autocovariance function is locally approximated by a 2-D Anisotropic Gaussian Kernel (AGK) in order to characterise texture by its local orientation and anisotropy. The estimation of texture parameters at a given scale is based on the Gradient Structure Tensor (GST) operator and does not require the explicit computation of the autocovariance. Finally, a new filter called AGK-MMSE that takes into account this spatial information is introduced and compared to the refined MMSE filter. The proposed filter shows better performances in terms of texture preservation and structure enhancement. I. INTRODUCTION In this paper, a new model for the two-point statistics of SAR intensity is presented. A parametric form for the 2-D autocovariance function is used to introduce in the previous models the notions of local orientation and spatial anisotropy. This simple model handles deterministic structures as well as the spatial correlation of heterogeneous clutter. This descrip- tion is then shown to be useful in the context of adaptive speckle filtering. The theoretical model is introduced in Section II, then a method for parameter estimation is presented in Section III. An enhancement of the traditional adaptive filters based on this model is presented in Section IV. Section V shows results on real SAR data. Finally, Section VI concludes the article.

This paper presents a first evaluation of the ESPRIT approach in polarimetric interferometric SAR. This evaluation is carried out by using 3D images obtained by SAR tomographic like an alternative to the acquisition of ground-truth data, which is an extremely complex task in the case of volume areas. All parameters over a volumetric area are directly visible in a tomographic image and can, therefore, be employed to validate the ESPRIT approach by comparing parameters generated by ESPRIT and the SAR tomography approach. This allows to identify the principal deficiencies of the ESPRIT method, which occur over high vegetation areas, where there is a misinterpretation of the ESPRIT results. Whereas, the ESPRIT approach is useful for building characterisation, identifying a good applicability area. Airborne L-band repeat-pass interferometric data of the German Aerospace Center (DLR) experimental airborne SAR are used to perform this evaluation.

This paper shows first results of polarimetric SAR stereo applied to urban areas. SAR stereo is known as a useful technique for three-dimensional mapping. Until now many approaches and results have been reported, based on single polarization measurements. But stereogrammetry has not been used with polarimetric information. In this paper, we use polarimetric SAR measurements acquired during a square loop flight. It is shown that typical objects, like a TV tower can be reconstructed and its polarimetric properties analysed using this method.

A new method to discriminate dry snow in alpine regions is presented. Due to the wide variety of Alpine environments, the scene under study is first segmented into surface and forested area classes from L and C-band summer polarimetric SAR data. Dry snow is then discriminated over each class using adapted methods. A new polarimetric multi- temporal optimization procedure, named PCVE, is proposed to increase the slight polarimetric contrast due to the presence of snow over surfaces. Snow covered forests are discriminated using performing polarimetric indicators resulting from a decomposition of incoherent matrix representations. The effectiveness of this method is demonstrated over a French alpine test site using SIR-C L and C-band polarimetric SAR data. I. INTRODUCTION The localization of dry snow in alpine environments using intermediate frequency SAR data (L and C-bands) still remains a problematic application (1). Indeed, at such frequencies, dry snow is a low attenuation medium and only slightly affects the backscattered signal amplitude. Moreover alpine areas are characterized by a wide variety of underlying media with changing characteristics and important topography that may strongly affect a scene response. Dry snow mapping is an important product for global snow monitoring, widely used in the frame of hydrological applications, like Snow Water Equivalent determination. This paper presents a polarimetric method to map dry snow extent in alpine areas using multi-frequency and multi- temporal polarimetric SAR data. Due to the variability of alpine environments the method is decomposed into three steps. A first part is dedicated to the classification of the scene into surface and forest types from summer data sets. The classification may be applied over both L and C-band data sets. The main advantage of the C-band summer classification is the possibility to lead the dry snow discrimination analysis at a single frequency band. Each media is then processed separately. The presence of snow is then detected over surfaces by means of a new optimization method, based on a Polarimetric Contrast Variation Enhancement (PCVE) (2). Snow covered forests are analyzed from summer to winter variations of polarimetric decomposition results at C-band. Merged discrimination results are finally analyzed through a quantitative estimation of the detection performance. II. TEST SITE AND SAR DATA The test region is located in southern French alps (N 44°.15' / E 7°.15') and was measured during the SIR-C campaign in April and October 1994. Multi-temporal fully polarimetric SAR data sets were acquired at L and C-bands in both snow free (October) and snow (April) conditions. The test sites, Risoul (300 km²) and Izoard (800 km²), are composed of three main alpine environments: high altitude unvegetated surfaces, medium to high altitude forested zones and low altitude valleys (Fig. 1). Simultaneously to radar acquisitions, ground truth measurements were carried out over the test sites (automatic and manual snow sample network). They are summarized in Table I. Various types of underlying media may be encountered over the considered sites: rocks, bare soils, forests and pastures. Both test sites are partly covered by frozen spring snow, due to the early morning acquisition time. The snow cover altitude ranges from 1200 m up to 3000 m where its depth reaches 2 m. In this paper, only the Risoul site results are presented (Fig. 1).

This letter proposes a building characterization technique for L-band polarimetric interferometric synthetic aperture radar (SAR) data. This characterization consists of building identification and height estimation. Initially, a polarimetric interferometric segmentation is performed to isolate buildings from their surroundings. This classification identifies three basic categories: single bounce, double bounce, and volume diffusion. In order to compensate for the misclassifications among the volume and the double-bounce classes, interferometric phases given by the high-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) method are analyzed. Once buildings are localized, a phase-to-height procedure is applied to retrieve building height information. The method is validated using E-SAR, German Aerospace Center (DLR) fully polarimetric SAR data, at L-band, repeat-pass mode, over the Oberpfaffenhofen, Germany, test site, with a spatial resolution of 1.5 m in range and azimuth. More than 80% of buildings are retrieved with acceptably accurate height estimates.

Radio interferences are becoming more and more an important source for image degradation in synthetic aperture radar (SAR) imaging. Especially at longer wavelengths, interferences are often very strong, and their suppression is required during data processing. However, at shorter wavelengths, interferences are often not obvious in the image amplitude, and filtering is not performed in an operational way. Nevertheless, interferences might significantly degrade the image phase, and the estimation of sensitive parameters like interferometric coherence or polarimetric descriptors becomes imprecise. Interference suppression is usually performed on the raw data, which are in most cases not available to the end-user. In this letter, a new interference suppression method for focused SAR images is proposed. Its performance is tested on interferometric repeat-pass data acquired by the German Aerospace Agency's experimental SAR system (E-SAR) at L-band.

The aim of this paper is to present two novel surface model based inversion algorithms using multifrequency and polSAR data. The first part of this work introduces a polarimetric scattering model using the integral equation model (IEM) with a transition model for the reflection coefficient. Polarimetric descriptors: the entropy (H), the anisotropy (A), and the mean alpha angle obtained from the Cloude/Pottier polarimetric decomposition theorem are employed for surface characterization. A new polarimetric parameter: the eigenvalue relative difference (ERD) is developed to overcome the anisotropy limitations in case of rough surfaces. Built on relevant polarimetric descriptors: H, alpha1 and ERD, two novel surface model based inversion algorithms using multifrequency polSAR data are presented. The first algorithm is built up in a "low frequency" (P to S band) - "high frequency" (C to K band) scheme. This algorithm retrieves the soil parameters by using multifrequency least-square fit

In this paper we present two methods to estimate the local orientation of spatial texture in polarimetric SAR data. The first approach is based on an original spatial parametric modelling of the 2D autocorrelation function, and was introduced in a previous paper. The second method is based on orientation interpolation between a reduced set of basis functions called steerable filters. Both are applied to measured polarimetric SAR data

This paper addresses the problem of moving and nonstationary objects analysis in SAR images. A method, based on Matching Pursuit (MP) algorithm is proposed to decompose the SAR signal into a set of chirplets. Chirplets parameters can be related to physical characteristics of the SAR scene constituents and used to produce a multidimensional polarimetric bright point model of the object of an observed object

This paper presents a new method to map dry snow in Alpine areas. A supervised discrimination algorithm, based on polarimetric contrast, is proposed to enhance multi-temporal and/or multi-frequency polarimetric behavior variations over snow-covered areas. This technique is shown to be more robust with respect to topography and underlying media diversity than classical contrast enhancement approaches. The effectiveness of the proposed method is demonstrated using SIR-C L and C-band polarimetric data sets

In this paper, we proposed an unsupervised terrain and land-use classification algorithm using polarimetric synthetic aperture radar data. Unlike other algorithms that classify pixels statistically and ignore their scattering characteristics, this algorithm not only uses a statistical classifier, but also preserves the purity of dominant polarimetric scattering properties. This algorithm uses a combination of a scattering model-based decomposition developed by Freeman and Durden and the maximum-likelihood classifier based on the complex Wishart distribution. The first step is to apply the Freeman and Durden decomposition to divide pixels into three scattering categories: surface scattering, volume scattering, and double-bounce scattering. To preserve the purity of scattering characteristics, pixels in a scattering category are restricted to be classified with other pixels in the same scattering category. An efficient and effective class initialization scheme is also devised to initially merge clusters from many small clusters in each scattering category by applying a merge criterion developed based on the Wishart distance measure. Then, the iterative Wishart classifier is applied. The stability in convergence is much superior to that of the previous algorithm using the entropy/anisotropy/Wishart classifier. Finally, an automated color rendering scheme is proposed, based on the classes' scattering category to code the pixels to resemble their natural color. This algorithm is also flexible and computationally efficient. The effectiveness of this algorithm is demonstrated using the Jet Propulsion Laboratory's AIRSAR and the German Aerospace Center's (DLR) E-SAR L-band polarimetric synthetic aperture radar images.

The aim of this paper is to present a surface model inversion using the integral equation formulation of backscattering coefficients. A quantification of the influence of surface parameters such as roughness and soil moisture on polarimetric indicators for various frequency bands is led. Finally, a technique is introduced to retrieve a surface RMS height and dielectric constant from multi-frequency data. The inversion technique is applied to polarimetric and multi-frequency measurements acquired at EMSL, JRC laboratory.

This paper introduces a study of the influence of the size of SAR resolution cell on polarimetric scattering characteristics over rough surfaces. Surface scattering is shown to be dependent on the cell size to correlation length ratio. SAR resolution is taken into account by dividing a surface spectrum in two parts: a low-frequency spectrum corresponding to local slopes and a high-frequency component, defining the roughness inside a resolution cell. Backscattering coefficients are calculated for each resolution cell with the IEM model using local incidence angles. Surface scattering is characterized with three polarimetric indicators H/A//spl alpha//spl I.bar/, highly related to the soil characteristics. These models are validated on indoor polarimetric SAR measurements acquired at the JRC laboratory.

In this overview, reasons are provided on why we do need to place multi-modal, multi-band single and multiple pass POLinSAR monitoring platforms into air and space. The questions "on what POLinSAR monitoring can provide that POL-SAR and IN-SAR by themselves cannot accomplish" is assessed; whereupon facts and justifications on placing POL-IN-BISAR satellite clusters into space are presented. Reasons for this technology becoming a basic requirement for current, near-future and much more so for future all day & night year-round monitoring of the terrestrial covers are analyzed in view of the un-abating and uncontrollable terrestrial population explosion, which has, does and for ever will result in unavoidable conflicts deteriorating unfortunately at times into terrorism. The pertinent questions on how to reduce the exorbitant cost for initiating this "home-globe security protection" technology are therefore also broached, and the expected benefits are laid out. The pertinent National and International airborne and space borne multi-modal, multi-band SAR remote sensing and security conflict surveillance support agencies are herewith invited for co-sponsoring our proposal, which is timely and fleets of orbiting multi-band POLinSAR platforms are urgently required to be placed into space.

The performance difference between full polarimetry (fp) and partial polarimetry (pp) systems is still an important open question according to Imbo and Souyris (2000) and Lee et al. (1995), especially in the frame of future low-cost spaceborne SAR studies. The aim of this paper is to briefly present a fully polarimetric model, the recent F-Bragg model (Breuer et al., (2002, 2003)), and compare it to its restriction to partial polarimetry. In a second step the inversion possibilities of fp and pp F-Bragg are investigated.

In this paper, a fully polarimetric analysis method is introduced to decompose synthesized interferometric SAR images into sub-aperture data sets, which correspond to the scene responses under different azimuthal look-angles. A statistical analysis of polarimetric parameters permits to clearly discriminate media showing a nonstationary polarimetric behavior during the SAR integration. A method is proposed to observe the polarimetric interferometric properties of anisotropic targets.

In this paper, a fully polarimetric analysis method is introduced to decompose synthesized polarimetric images into sub-aperture data sets, which corresponds to the scene responses under different azimuthal look-angles. A statistical analysis of polarimetric parameters permits to clearly discriminate media showing a non-stationary behavior during the SAR integration. A method is proposed, which eliminates the influence of azimuthal backscattering variations in conventional polarimetric SAR data analysis.

Abstract—This paper describes the analysis of built-up areas using fully polarimetric interferometric SAR data at L-band. This approach uses a polarimetric interferometric segmentation to determine the number of dominant scattering mechanisms required by an interferometric phase estimation using ESPRIT method. I.

This paper introduces a qualitative characterization of dry snow scattering behaviour in Alpine areas. An electromagnetic scattering model is developed, based on the vector radiative transfer equation, to simulate the fully polarimetric response of a snow cover. Multi-temporal and multi-frequency polarimetric SAR data acquired over Alpine test sites are analyzed by comparing polarimetric indicators to simulated ones with various sets of snow cover characteristics.

In synthetic aperture radar (SAR) polarimetry, the measured polarimetric signatures are used to analyze physical scattering properties of the imaged media. It is generally assumed that the sensor has a fixed orientation with respect to the objects. However, SAR sensors operating at lower frequencies, like L- and P-band, have a wide azimuth beamwidth, i.e., during the formation of the synthetic aperture, multiple squint angles are integrated to build the full-resolution SAR image. Variations in the polarimetric properties with the azimuthal look angle remain unconsidered. In this paper, a fully polarimetric subaperture analysis method is introduced. Using deconvolution, synthesized SAR images are decomposed into subaperture datasets, which correspond to the scene responses under different azimuthal look angles. A statistical analysis of the polarimetric parameters permits to clearly discriminate media showing a nonstationary behavior during the SAR integration. Finally, a method is proposed, which eliminates the influence of azimuthal backscattering variations in conventional polarimetric SAR data analysis. The effectiveness of the new methods is demonstrated on fully polarimetric SAR data, acquired by the German Aerospace Center (DLR) airborne experimental SAR sensor (E-SAR) at L-band.

The aim of this paper is to analyse the information contained in multi-frequency and polarimetric SAR data for an accurate retrieval of surface geophysical parameters. A two-scale surface scattering model is presented with the aim to develop an inversion algorithm. This model is represented by projection into a three-dimensional space defined by the H-A-/spl alpha/_parameters, which permits, for each observation frequency value, to obtain distinct curves with respect to the soil moisture content and large-scale roughness. Then, a comparison between multi-frequency and polarimetric data from the JRC laboratory and the two-scale surface polarimetric response is carried out. At last, two different multi-frequency parameter inversion methods based on artificial neural networks schemes are proposed.

In this paper is introduced an approach to the classification and interpretation of SAR data using the complementary polarimetric and interferometric information. An unsupervised polarimetric segmentation is applied to one of the separate interferometric dataset. The use of pertinent polarimetric indicators permits to give an interpretation of each resulting cluster scattering mechanism and to classify the observed scene into three canonical scattering types. The interpretation and the segmentation of an optimized interferometric coherency spectrum is applied, for each type of scattering, to initialize an unsupervised statistical interferometric classification procedure.

In this paper is introduced a fully polarimetric sub-aperture analysis method. A deconvolution technique is developed in order to decompose synthesized SAR images into sub-aperture data sets which correspond to the scene global response observed under different azimuthal look angles. A polarimetric variation analysis is achieved, using pertinent parameters, to determine the nature of the non-stationary scattering mechanisms. A statistical analysis of the polarimetric parameters permits to clearly discriminate the media showing a varying behavior during the SAR integration. Decomposition and analysis techniques are applied to data acquired by the DLR airborne E-SAR sensor at L band.

Introduces a new classification scheme for dual frequency polarimetric SAR data sets. A (6/spl times/6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6/spl times/6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the cross-correlation matrix. These classification schemes are applied to full polarimetric P, L, and C-band SAR images of the Nezer Forest, France, acquired by the NASA/JPL AIRSAR sensor in 1989.