For the first time, co-located wind vectors and wave spectral characteristics are available thanks to the French/Chinese CFOSAT mission, which includes a wind scatterometer SCAT and a wave scatterometer SWIM. Three years after its launch, CFOSAT data is thoroughly qualified and various scientific work has been undertaken. This paper focuses on CFOSAT SWIM data, its performance and scientific contribution to oceanography, coastal and sea ice study.

Access to fresh water is a key issue for the next decades in the context of global warming. The water level of lakes is a fundamental variable that needs to be monitored for this purpose. The radar altimetry constellation brings a worldwide means to this question. Recent advances in radar altimeter onboard tracking modes have allowed monitoring thousands of lakes and rivers. Now, measurements are widely available with better resolution: it is time to drastically improve the processing. The altimetry waveforms over lakes are difficult to analyze and very different from the ocean ones. We face a large variety of signals due to surface roughness, lake geometry, and environment. The inversion process, named retracking, shall be able to describe all these components. We propose here a retracking based on physical simulations taking as inputs the lake contour and the instrument characteristics. Fitting the simulation on the waveforms gives the water surface height. The algorithm has been tested on the Sentinel-3A and Sentinel-3B time series over Occitan reservoirs (France) and Swiss lakes and compared toin situreferences. Over small Occitan reservoirs (few ha to few km2), the unbiased root-mean-square error (ub-RMSE) is better than 14 cm. Over the medium-size Swiss lakes, the ub-RMSE is better than 10 cm for most of them.

Altimetry missions provide very valuable measurements on hydrological bodies, although they have initially been built for ocean topography. The extension of their acquisition modes provides tens of thousands of lake and river observations thanks to recent OLTC table upgrades. The inversion of the waveforms is yet tricky because of mixed soil/water response, highly variable dynamics and surface roughness variations. In this paper, a physical retracking based on a priori simulation is proposed for Water Surface Height (WSH) estimation over lakes. The method is applied on Sentinel-3 SAR data. The results are illustrated and discussed over the Constance lake which is covered by three S3 tracks. Consistent estimations are obtained between the three with an unbiased RMSE better than 6cm. Extensive validation over Swiss lakes (19 tracks) has also been made leading to a median unbiased RMSE of 6cm.

The Chinese-French oceanography satellite, CFOSAT, was launched on October 2018. Two Ku-band scatterometers are on-board: SCAT for the wind observation and SWIM for the wave observation. After a first phase mainly dedicated to validation and identification of improvement possibilities, the ground processing and products generated were upgraded. This paper presents the main evolutions implemented and their positive impacts on the SWIM data quality.

This article describes the first results obtained from the Surface Waves Investigation and Monitoring (SWIM) instrument carried by the China France Oceanography Satellite (CFOSAT), which was launched on October 29, 2018. SWIM is a Ku-band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross section, and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in detail. The normalized radar cross section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range [70-500 m] is illustrated. The main limitations are discussed, and the perspectives to improve the data quality are presented.

The accuracy of sea surface parameters retrieved from altimeter missions is predominantly governed by the choice of the so-called “retracking” algorithm, i.e., the model and inversion method implemented to obtain the surface parameters from the backscattered waveform. For continuity reasons, the choice of space agencies is usually to apply the same retracker from one satellite mission to the other to ensure long-time homogeneous series. In this article, taking the opportunity of a new configuration of the nadir pointing measurements onboard the recently launched China France Oceanography Satellite (CFOSAT) with the Surface Waves Investigation and Monitoring (SWIM) instrument (Hauseret al.,2020), the retracking method was upgraded, by implementing a novel algorithm, called “Adaptive” retracker. It combines the improvements brought by Poissonet al.,(2018) for the estimation of surface parameters from peaked waveforms over sea ice, improvements in the way the instrumental characteristics are considered in the model (mispointing, point target response) and a more accurate consideration of speckle statistics. In this article, we first show from simulations carried out in the instrumental configuration of SWIM that the Adaptive algorithm has better accuracy and performance than the classical MLE4 algorithm. Then, the geophysical parameters obtained with real data from SWIM are analyzed with comparisons to reference data sets (model and products from altimeters). We show that this new algorithm has several benefits with respect to the classical MLE4 method: no need of lookup tables to correct biases, significant noise reduction on all geophysical variables especially the significant wave height, and performance of inversion over a large set of echo shapes, resulting from standard oceanic scenes as well as highly specular conditions such as over bloom or sea ice.

The Chinese-French oceanography satellite, CFOSAT, was launched on October 2018. Two Ku-band scatterometers are on-board: SCAT for the wind observation and SWIM for the wave observation. This paper presents the most recent results on the SWIM data quality analysis a few months after the end of the CAL/VAL phase.

The China-France Oceangraphy SATellite (CFOSAT) is an innovative satellite mission with wind and waves measurements on oceans. This paper aims to evaluate the first results of the assimilation of the wave data provided by CFOSAT in the wave model MFWAM. Model runs are implemented during the Calibration/Validation phase of the mission. The results are compared to the wave data from altimeters and buoys. The first results are promising and indicate a significant improvement of wave heights in the different ocean basins (high latitudes, intermediate latitudes and the tropics). Azimuthal cut-off sensitivity tests for the SWIM wave spectra are also examined in this study. We also discussed the impact of combined wave spectra and the ones from the incidence angles of SWIM (6, 8 and 10°). In other respects this study also investigate the complementary use of SWIM and SAR from CFOSAT and SAR from Sentinel-1 for combined assimilation in the wave model MFWAM

This paper presents the goal, organization and first results of the calibration and validation activities devoted to the Ku-band wave scatterometer SWIM in operation since the end of October 2018 after the launch of the China-France Oceanography SATellite CFOSAT.

The increasing number of directional wave spectra from satellites is a very important opportunity for the improvement of operational wave forecast. This will lead to a better understanding of swell dissipation which is still a top task in wave modeling. This work aims to investigate how the assimilation of different sources of directional wave spectra will impact the analysis and forecast of wave parameters. Two satellites, sentinel-1A and 1B, provide the level 2 SAR wave spectra, which have been qualified for operational use regarding to the calibration/validation phase developed in the last two years. This year the launch of the satellite CFOSAT will ensure a third source directional wave spectra from the real aperture radar SWIM and a significant wave height at nadir look. Synthetic wave data of SWIM and wave spectra from S1A and S1B are assimilated conjointly in the wave model MFWAM for 1 cycle of 13 days during the southern winter season. Two sets of synthetic SWIM data with and without instrumental errors have been tested in the assimilation. The validation of the results with altimeters significant wave heights shows a significant impact in the periods of analysis and forecast. In this study we also investigated the impact of instrumental errors provided by SWIM simulator and the wavelength cut-off on the assimilation results.

This paper is on the retrieval of spectral properties of surface ocean waves from the real-aperture radar KuROS carried on an airplane. The system was designed to prepare the satellite mission CFOSAT and to contribute to its validation. In addition, KuROS provides Doppler measurements which are analyzed as Doppler velocity spectra co-located with the wave height spectra derived from intensity measurements. Data have been acquired from 2013 to 2017 in various circumstances including fetch-limited, swell, or mixed sea conditions and under moderate to high wind conditions. In this paper we present results on the directional spectra obtained and on the associated integral parameters such as significant wave height, peak direction, peak wavelength (or frequency), directional and frequency spreads. Comparisons with outputs of the wave model hindcast (MFWAM model) are presented. We also show some results on the Doppler fluctuation spectra.

The wave scatterometer, SWIM, will be on-board the Chinese French oceanographic mission, CFOSAT. It will complete the Chinese wind scatterometer, SCAT. They will provide the scientific community with joint wind and wave measurements for the very first time. The launch is now coming soon: autumn 2018. SWIM is a Ku-band real-aperture radar with 6 rotating fan-beams pointing near nadir. The main characteristics and performance of the instrument measured during the Satellite Integration Campaign, products and ground segment development are presented in this paper, as well as predicted performances estimated from simulations.

This paper provides an overview of the surface waves investigation and monitoring (SWIM) instrument which will be one of the two payload instruments carried by China France Oceanography SATellite (CFOSAT) with a planned launch date in mid-2018. SWIM is a real aperture wave scatterometer operated at near-nadir incidence angles and dedicated to the measurement of directional spectra of ocean waves. The SWIM flight model is currently being assembled and tested, its performance is being assessed and its prototype data processing algorithm is being developed. The aim of this paper is to provide a complete overview on the motivations and scientific requirements of this mission, together with a description of the design and characteristics of the SWIM instrument, and the analysis of its expected performances based on a prelaunch study. An end-to-end simulator has been developed to evaluate the quality of the data products, thus allowing the overall performance of the instrument to be assessed. Simulations run with two subsets of full orbit subsets show that the performances of the instrument and the inversion algorithms will meet the scientific requirements for the mission.

With a low cut-off for the directional wave spectra CFOSAT will impact both mixed sea and swell. The complementary use of CFOSAT and SAR wave spectra will enhance the impact of the assimilation and therefore induce a more accurate sea state. The CFOSAT mission is on one hand a big challenge for operational wave forecasting system, and on the other hand a good opportunity for wave modellers in order to improve the source and dissipation terms in the wave model.

SWIM is part of the CFOSAT satellite mission payload. It is designed for the measurement of directional ocean wave spectra. It is a Ku-band real-aperture radar with 6 rotating fan-beams pointing near nadir. The main characteristics of the instrument, data, products are presented in this paper, as well as performances estimated from simulations.

The Chinese and French Space Agencies are jointly preparing an innovative mission, CFOSAT (China France Oceanography Satellite) devoted to the monitoring of the ocean surface and its related science and applications. This paper gives an overview of the scientific objectives, the mission and instrument characteristics, the expected data products and their performance.

This paper presents the under-going development of the ground segment algorithms of the SWIM instrument. SWIM is a wave scatterometer which will be embarked on the Chinese French oceanography mission, CFOSAT. SWIM aims at measuring the 2D oceanic wave spectra; it is a Ku band real aperture radar. Simulations are performed to get data along the satellite track: radar signals are obtained simulated interaction with a realistic sea surface and taking into account the radar geometry. Then, the simulated data are processed with software prototypes.

We have designed and developed a new airborne Ku-band Doppler radar, called KuROS, to prepare the CFOSAT satellite mission for measuring ocean surface wind and waves. The main characteristics of this new radar are presented, and first results obtained from a campaign held in 2013 illustrated. Both intensity and Doppler information are used to estimate the directional spectra of ocean waves. Radar cross-section and directional spectra are assessed trough comparisons with independent information.

The CFOSAT mission is an innovative spatial mission for oceanography: for the very first time, both wind and wave vectors will be measured at the global ocean surface. This paper presents the wave scatterometer, SWIM and its associated scientific performances.

SWIM (Surface Wave Investigation and Monitoring) is a spaceborne radar pointing at nadir and small incidence angles, scanning in azimuth. It is designed for the measurement of directional ocean wave spectra and will be embarked on the CFOSAT (China France Oceanography SATellite) mission to be launched in 2014. The CFOSAT project is now in the C/D phase (manufacturing phase). Taking into account the very last definition of the instrument, we present here the expected performances for estimates of significant wave height and spectral parameters of long ocean waves. These performances have been obtained using numerical simulations taking into account instrument specifications and realistic sea surface conditions, in particular those corresponding to the Atlantic storm of November 2002, which caused the Prestige ship sinking and its terrible oil slick.

This letter presents a case study addressing the comparison between different synthetic aperture radar (SAR) partial polarimetric options for tropical-vegetation cartography. These options include compact polarization (CP), dual polarization (DP), and alternating polarization (AP). They are all derived from fully polarimetric (FP) SAR data acquired by the airborne SAR (AIRSAR) sensor over the French Polynesian Tubuai Island. The classification approach is based on the support vector machine algorithm and is further validated by several ground surveys. For a single frequency band, FP data give significantly better results than any other partial polarimetric configuration. Among the partial polarimetric architectures, the CP mode performs best. In addition, the DP mode shows better performance than the AP mode, highlighting the value of the polarimetric differential phase. The combination of different frequency bands (P-, L-, and C-bands) holds the most significant improvement: The multifrequency diversity adds generally more information than the multipolarization diversity. A noticeable result is the major contribution of the C-band at VV polarization (the only polarization available at C-band with the AIRSAR data set used in this letter) to the classification performance, due to its ability to discriminate between Pinus and Falcata.

In this paper, a time-frequency analysis (TFA) is proposed to derive the backscattering properties of each pixel in single-polarization synthetic aperture radar (SAR) images. At high resolution (HR), some backscattering variations which are linked to the scene geometry and the surface property occur during the radar acquisition. TFA permits to retrieve these variations from the synthesized images. The proposed TFA algorithm is based on a sliding bandpass filtering in the Fourier domain, from which a spectrogram featuring the range and azimuth backscattering variations is derived. The spectrograms summarize the physical properties of each pixel. From the spectrogram analysis, four target classes representing the four main kinds of backscattering behaviors observed in SAR images are defined: frequency invariant, range variant, azimuth variant, and 2-D variant. These classes can further be linked to the physical properties of the objects. An original and simple set of five features estimated from spectrograms is proposed to classify point targets into these four classes. A performance assessment of this classification is carried out, using ONERA/RAMSES X-band airborne images acquired over the city of Toulouse, France. A robustness analysis is also conducted, in order to assess the impact of incidence angle and resolution on the classification performance. Finally, results are also given for spaceborne images (TerraSAR-X spotlight images). The physical interpretation developed in airborne case appears to be also valid for metric spaceborne data. After studying the TFA on HR spaceborne images, the tradeoff between HR coupled with TFA and medium resolution coupled with polarimetric analysis is investigated. Actually, TFA represents another way of characterizing the physical mechanisms involved in image formation.

In the frame of the development of the instrument SWIM (Surface Waves Investigation and Monitoring) on the CFOSAT program (Chinese French Oceanographic Satellite) funded by CNES, Thales Alenia Space is currently developing a new multi beam rotating antenna in Ku Band. This single reflector offset antenna includes a rotating feed comprising 6 beams. The SWIM instrument is the first ever space radar concept that is mainly dedicated to the measurement of ocean waves directional spectra and surface wind velocities through multi-azimuth and multi-incidence observations. Orbiting on a 500 km sun-synchronous orbit, its multiple Ku-band (13,575 GHz) beams illuminating from nadir to 10° incidence and scanning the whole azimuth angles (0-360°) provide with a 180 km wide swath and a quasi global coverage of the planet between the latitude of -80 and 80°. Such a wide range of observations requiring high range resolution (about 20 m on the ground) have led to design an instrument whose architecture and technology goes beyond what has been done on altimeter and scatterometer systems. At antenna subsystem level, multi-azimuth and multi-incidence observations requirements have led to design an ambitious antenna subsystem that rotates at 6 rotations per minute while transmitting RF signals towards 6 differents beams in Transmit and Receive Modes. Thales Alenia Space started in January 2009 under CNES contract phase B studies on the design of this multi beam rotating antenna in order to contribute to the System Preliminary Design Review held successfully in January 2010. B Phase complementary activities are currently under progress to prepare the C/D Phase planned to start beginning of 2011. This paper aims at giving an overview of the SWIM antenna preliminary design and performances.

SWIM is a Ku-band radar designed for wave directional spectrum estimation. This radar operates at six incidence angles (from 0° to 10°) with a complete azimuth scanning covering a swath of 180 km. The phase B (addressing preliminary design) of SWIM is currently under finalization. In, the preliminary design and associated performance analysis have been published taking into account the first results of Phase B design. This paper is focused on the last performance assessment of this phase B for all the measurements performed by the SWIM instrument.

This paper presents an application of the recent advances in the field of spherically invariant random vector (SIRV) modeling for coherency matrix estimation in heterogeneous clutter. The complete description of the polarimetric synthetic aperture radar (POLSAR) data set is achieved by estimating the span and the normalized coherency independently. The normalized coherency describes the polarimetric diversity, while the span indicates the total received power. The main advantages of the proposed fixed-point (FP) estimator are that it does not require anya prioriinformation about the probability density function of the texture (or span) and that it can directly be applied on adaptive neighborhoods. Interesting results are obtained when coupling this FP estimator with an adaptive spatial support based on the scalar span information. Based on the SIRV model, a new maximum-likelihood distance measure is introduced for unsupervised POLSAR classification. The proposed method is tested with both simulated POLSAR data and airborne POLSAR images provided by the Radar Ae¿roporte¿ Multi-Spectral d'Etude des Signatures system. Results of entropy/alpha/anisotropy decomposition, followed by unsupervised classification, allow discussing the use of the normalized coherency and the span as two separate descriptors of POLSAR data sets.

This paper presents an application of DInSAR techniques for the assessment of ground subsidences around Tunis City. A longterm analysis using two interferometric techniques were carried out to attempt reliable measurements. In this work, some aspects of interferometric processing softwares are reviewed and possible improvements are proposed in order to get better results. The convergence of the two different interferometric techniques done simultaneously and independently confirms results accuracy.

The instrument SWIM (Surface Waves Investigation and Monitoring) on the CFOSAT program (Chinese French Oceanographic Satellite) is a state of the art radar for several reasons. At first, SWIM is the first ever space radar concept that is mainly dedicated to the measurement of ocean waves directional spectra and surface wind velocities through multi-azimuth multi-incidence observations. Orbiting on a 500 km sun-synchronous orbit, its multiple Ku-band (13, 575 GHz) beams illuminating from nadir to 10° incidence and scanning the whole azimuth angles (0-360°) provide with a 180 km wide swath and a quasi global coverage of the world between -80 and 80°. Secondly, such a wide range of observations requiring high range resolution (about 20 m on the ground) have led to design an instrument whose architecture and technology goes beyond what has been done on altimeter and scatterometer systems. The global coverage and the reduction of telemetry budgets have required to perform onboard range compression. The variety of signals at different incidences, the impact of the complex moving geometry of observation and the required real-time signal processing have led to propose onboard complete digital range compression on backscattered 320 MHz bandwidth signals. Finally, multi-azimuth multi-incidence observations requirements have led to design a complex antenna subsystem that rotates at 6 rounds per minute while transmitting high power RF signals towards tunable directions.

The increasing quality of satellite images has generated interests in extracting man-made structures in urban areas, such as buildings and roads. A classification adapted to urban areas can help to identify these structures. In this paper, SAR information are used to improve land-cover classification. We proposed a classification process using both radar and optical data, a segmentation and a classification with Support Vector Machines (SVM).

Time-frequency analysis (TFA) is an efficient tool to jointly detect point scatterers in SAR images and determine their backscattering properties. In this paper, we focus on TFA in multi-image context (for instance, interferometric stacks or multi-temporal series). A new TFA algorithm is proposed based on ¿spectrograms¿ which are 4D hyper images representing the behaviour of each pixel with respect to range and azimuth frequencies. Spectrograms are analyzed in a multi-image context: they are combined to select pixels with stable behaviour over time (persistent scatterer detection) or unstable behaviour (change detection). Spectrograms characterize short term variations, whereas, in this paper, we characterize long term variations based on spectrogram properties. Preliminary results obtained with Spotlight interferometric TerraSAR-X images are discussed.

SWIM is a Ku-band radar designed for wave directional spectrum estimation. This radar operates at six incidence angles (from 0° to 10°) with a complete azimuth scanning. SWIM is currently in Phase B (concept and design phase). In [1, 2], the preliminary design and associated performance analysis have been published taking into account the end of Phase A design. This paper is focused on the performance assessment of the SWIM instrument based on the new developments which occur during Phase B. In addition, major reviews have been carried out on the performance analysis.

The objective of this paper is twofold: first, to assess the potential of radar data for tropical vegetation cartography and, second, to evaluate the contribution of different polarimetric indicators that can be derived from a fully polarimetric data set. Because of its ability to take numerous and heterogeneous parameters into account, such as the various polarimetric indicators under consideration, a support vector machine (SVM) algorithm is used in the classification step. The contribution of the different polarimetric indicators is estimated through a greedy forward and backward method. Results have been assessed with AIRSAR polarimetric data polarimetric data acquired over a dense tropical environment. The results are compared to those obtained with the standard Wishart approach, for single frequency and multifrequency bands. It is shown that, when radar data do not satisfy the Wishart distribution, the SVM algorithm performs much better than the Wishart approach, when applied to an optimized set of polarimetric indicators.

Illustrations of parameters obtained from polar decomposition derived from fully polarimetric data acquired by ALOS-PALSAR over the Mai-Ndombo lake, in the Democratic Republic of Congo are presented. Results show their complementarity to usual polarimetric indices, such as the entropy or the alpha parameters

SWIM is a Ku-band radar designed for wave directional spectrums estimation. This radar operates at six incidence angles (0deg to 10deg) with complete azimuth scanning. This paper presents the simulation tool developed for defining SWIM design and evaluating SWIM performance. The simulation tool is an end-to-end simulator, i.e. from the sea surface to the estimated wave spectrum. Some simulations are discussed, showing that the preliminary design of SWIM will fulfill the scientific requirements.

This paper presents an application of the recent advances in the field of Spherically Invariant Random Vectors modelling. We propose the use of the Fixed Point (FP) estimator for deriving normalized polarimetric coherency matrices in compound Gaussian clutter. The main advantages of the FP estimator are that it does not require any "a priori" information about the probability density function of the texture and it can be directly applied on adaptive neighborhoods. Interesting results are obtained when coupling this FP estimator with an adaptive spatial support driven on the scalar span information. The proposed method is tested with both simulated POLSAR data and high resolution POLSAR data acquired over the French Alps.

This paper focuses an the multi-look polar decomposition of SAR images. Standard polar decomposition is generally used in single look polarimetry to decompose a bi-static or mono-static polarimetric scattering matrix into a product of an Hermitian matrix (boost) and a unitary matrix (rotation). An extension of this use in the framework of multi-looked polarimetry is proposed here. This new approach consists in decomposing the scattering matrix into boost and rotation components before vectorisation, then in averaging to generate boost and rotation coherency matrices separately, with new inferred parameters; the boost and rotation entropies, and concurrent dominant scattering mechanisms (alpha boost and alpha rotation). Tins multi-look extension of polar decomposition may allow for the definition of a new classification strategy for remote sensing data.

This paper addresses object recognition problem in SAR images with SVM classifier; the work has been mainly focused on feature vector definition. Actually, each object is represented by a feature vector and SVM aims to estimate the best hyperplanes that separate classes in the feature space. Very robust definition of feature vector is proposed and tested on real data (MSTAR database). Confusion matrices prove that a very good recognition rate is reached, even for mixed incidence angles configuration.

This paper focuses on polar decomposition, which is based on the quaternion formalism, in single-look and multilook synthetic aperture radar polarimetry. Polar decomposition is used to decompose a bistatic or monostatic polarimetric scattering matrix into a product of a Hermitian matrix (boost) and a unitary matrix (rotation). After an overview of polar decomposition principle and quaternion properties, coherent (single-look complex) and incoherent (multilook) polar decompositions are discussed. In single-look polar decomposition, we introduce the boost parameter and the rotation parameter with the purpose of classifying scattering mechanisms of different natures. New relationships between these geometrical parameters and the scattering matrix elements are obtained. We also briefly reexamine the standard coherent polarimetric target decomposition algorithms in the light of quaternions. Next, an original use of polar decomposition for incoherent polarimetric imaging is proposed, which leads to the definition of the multilook boost parameter and of the degree of polarization dispersion. Subsequently, a new approach is presented, which consists in decomposing the scattering matrix into boost and rotation components before vectorization, then in averaging to generate boost and rotation coherency matrices separately. This leads to new inferred parameters: the boost and rotation entropies, and the concurrent dominant scattering mechanisms. The link between these new parameters and standard polarimetric invariants from the Cloude and Pottier decomposition is discussed. Eventually, the multilook extension of polar decomposition may allow this to be applied to the classification of remote sensing data. In this framework, a set of five parameters reducing to four in the monostatic case can be considered.

The retrieval of 3-D surface models of the Earth is a major issue of remote sensing. Some nice results have already been obtained at medium resolution with optical and radar imaging sensors. For instance, missions such as the Shuttle Radar Topography Mission (SRTM) or the SPOT HRS have provided accurate digital terrain models. The computation of a digital surface model (DSM) over urban areas is the new challenging issue. Since the recent improvements in radar image resolution, synthetic aperture radar (SAR) interferometry, which had already proved its efficiency at low resolution, has provided an accurate tool for urban 3-D monitoring. However, the complexity of urban areas and high-resolution SAR images prevents the straightforward computation of an accurate DSM. In this paper, an original high-level processing chain is proposed to solve this problem, and some results on real data are discussed. The processing chain includes three main steps, namely: (1) information extraction; (2) fusion; and (3) correction. Our main contribution addresses the merging step, where we aim at retrieving both a classification and a DSM while imposing minimal constraint on the building shapes. The joint derivation of height and class enables the introduction of more contextual information. As a consequence, more flexibility toward scene architecture is possible. First, the initial images (interferogram, amplitude, and coherence images) are converted into higher-level information mapping with different approaches (filtering, object recognition, or global classification). Second, these new images are merged into a Markovian framework to jointly retrieve an improved classification and a height map. Third, DSM and classification are improved by computing layover and shadow from the estimated DSM. Comparison between shadow/layover and classification allows some corrections. This paper mainly addresses the second step, while the two others are briefly explained and referred to already published papers. The results obtained on real images are compared to ground truth and indicate a very good accuracy in spite of limited image resolution. The major limit of DSM computation remains the initial spatial and altimetric resolutions that need to be made more precise

Yhis study comes within the framework of the global cartography and inventory of the Polynesian landscape. An AIRSAR airborne acquired fully polarimettric data in L and P bands, in August 2000, over the main Polynesian Islands. This study focuses on Tubuai Island, where several ground surveys allow the validation of the different results. Different decompositions, such as H/A/alpha , or based on the Pauli formalism have shown their potential for land use discrimination. In order to take into account these different parameters into a supervised classification scheme, the SVM (Support Vector Machine) method is investigated. When dealing with only the coherent matrix elements, the results show that the SVM classification gives comparative results to those obtain with Wishart classification. Results are significantly improved when adding to the coherent matrix elements, other polarimetric parameters, as H/A/alpha or the co-polarized circular polarization correlation coefficient, rhorrll, for the Support Vector definition. Finally the best results are given when merging all the parameters for P and L bands, in addition to the only VV single channel acquired in C band.

We assess here the use of polar decomposition in SAR polarimetry, based on the quaternion formalism. Quaternions are helpful to obtain compact and original expressions of polar decomposition parameters, with straightforward extensions in bi-static polarimetry. Once the formalism of quaternions has been presented, both coherent and incoherent polarimetry approaches are discussed in this joint framework of polar decomposition and quaternions. Preliminary tests and comparisons with Cloude-Pottier parameters are conducted on ONERA/Ramses SAR data takes.

In this letter, we show that the polarimetric behavior of point targets is preserved even in the case of multipolarization incoherent acquisitions. Point targets are defined as targets embedded in one image pixel and presenting a very stable backscatter during the integration time. We discuss in particular how the polarimetric response restoration can be helpful for point target detection and analysis from such acquisitions (e.g., ASAR Alternate Polarization mode of ENVISAT), but also for permanent scatterers interferometry applications.

This letter aims at the extraction of roads and road networks from high-resolution synthetic aperture radar data. Classical methods based on line detection do not use all the information available; indeed, in high-resolution data, roads are large enough to be considered as regions and can be characterized also by their statistics. This property can be used in a classification scheme. Therefore, this letter presents a road extraction method which is based on the fusion of classification (statistical information) and line detection (structural information). This fusion is done at the feature level, which helps to improve both the level of likelihood and the number of the extracted roads. The proposed approach is tested with two classification methods and one line extractor. Results on two different datasets are discussed.

Synthetic aperture radar (SAR) interferometry enables to compute an height map of the scene which is useful for many applications. Yet the complexity of high resolution SAR images and of urban areas prevents from computing an accurate DSM easily and a high level processing chain is thus required. In this article, we propose a Markovian fusion scheme to retrieve jointly the height map and the classification. The original data (amplitude, interferogram and coherence) are first processed in order to get new entries. They represent advanced information on the scene, extracted with different approaches (filtering, object recognition or global classification). These features are then merged in a Markovian framework to recover an improved classification and height map. The method is illustrated on real data.

Three-dimensional reconstruction in urban areas is one of the major issues in remote sensing applications. SAR interferometry (InSAR) has a great potential to provide Digital Surface Models (DSM), but the context of urban areas and high resolution (HR) is difficult (geometrical distortions, surface heterogeneities, speckle, height discontinuities). Previous studies prove both the difficulty and the potential of the method. In this paper, we propose a new fusion method to compute DSM from HR InSAR. The merging approach enables to take into account several kinds of information extracted from the original data in order to retrieve jointly a classification and a DSM. The paper will be focused on the analysis of the results in order to understand the limits and potentials of InSAR over urban areas. Before discussing the results, the method will be briefly described.

Discontinuous objects, such as buildings, produce shadows in SAR images. Shadows are striking features which greatly help in the image understanding. Due to the high density of buildings in urban areas, shadows cover a large part of the image and provide a major hint to build a map of the city. A straightforward use of the shadows is to determine the building height from the shadow dimensions. We propose another approach here which makes use of the shadow to help in detecting the building itself when a high resolution interferogram is available. Starting from an amplitude image with very high definition and the corresponding interferogram, we model the building detection problem as an energy minimization where the interaction between a building and its shadow is taken into account. The method allows to obtain excellent detections especially for high or isolated building, despite the important noise level.

We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for high-resolution SAR images over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian scattering statistics (K, Weibull, Log-normal, Nakagami-Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. As a consequence, we use a mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data. This estimation method is based on the second-kind statistics, which are detailed in the paper. We also prove its accuracy for urban areas at high resolution. The quality of the classification that is obtained by mixing this model and a Markovian segmentation is high and enables us to distinguish between ground, buildings, and vegetation.

The framework of this study is classification of high resolution SAR images over urban areas. Statistics of these images reflect the presence of strong reflectors scattered all over; therefore histograms have a heavy tail. We propose a new distri- bution model (Fisher distribution) to fit such probability density functions. As its moments are not defined for all parameter values, we use a second kind statistics based estimation (log- moment estimation). The purpose of this article is the validation of both estimation method and distribution model. We first prove that, in this context, log-moment method is more accurate than moment method. We also demonstrate that Fisher functions are the most accurate for man-made structures. Finally these distributions are used in a Markovian classification.