In this letter, we present a new approach to the analysis of polarimetric radar time series, when interest centers on the changes in the full$n \times n$coherency matrix of the scene rather than a scalar backscatter coefficient. The key to the new idea is to form matrix differences over time, but always constrained so that the remainder is a physical coherency matrix, that is, formed by some combination of physical scattering mechanisms in the scene. We first detail the algorithm and compare it with other published approaches before illustrating its application to the L-band POLSAR data from the NASA-Jet Propulsion Laboratory (JPL) uninhabited aerial vehicle SAR (UAVSAR) system for study of agricultural changes around Saskatoon, Canada, over a two-year baseline.

This study proposes, for the first time, an approach to remove thermal noise from the wave coherency matrix,$\mathrm {C_{2}}$, estimated from single-look complex dual-polarization Interferometric Wide Swath mode Sentinel-1 synthetic aperture radar data. The approach is straightforward; it exploits the ThermalNoiseRemoval module, provided by the European Space Agency (ESA) in its Sentinel Application Platform (SNAP) software, to remove thermal noise from the channel intensities. Then, noise correction on the complex data is applied, in order to estimate the noise-free$\mathrm {C_{2}}$matrix. As a further novelty, the proposed approach can be implemented in SNAP, through the use of a processing graph that is here provided. The method is applied on a dense time series of Sentinel-1 data, collected on an agricultural area located near Seville, Spain. The impact of thermal noise on the estimation of the eigendecomposition parameters of$\mathrm {C_{2}}$, i.e., entropy ($H_{2}$), average alpha angle ($\overline {\alpha _{2}}$), and anisotropy ($A_{2}$), is assessed for different land-cover types, namely river, rice, forest, and urban areas. Monte Carlo simulations are implemented to assess the performance of the proposed approach in estimating$H_{2}$,$\overline {\alpha _{2}}$, and$A_{2}$. Results show that the proposed noise removal method improves the estimation of these parameters for the considered land-cover classes.

In this study, we advance a new family of model-based decompositions adapted for dual-pol synthetic aperture radar data. These are formulated using the Stokes vector formalism, coupled to mappings from full quad-pol decomposition theory. A generalized model-based decomposition is developed, which allows separation of an arbitrary Stokes vector into partially polarized and polarized wave components. We employ the widely used random dipole cloud as a volume model but, in general, nondipole options can be used. The cross-polarized phase$\delta $, and the$\alpha $angle, which is a function of the ratio between wave components, measure the transformation of polarization state on reflection. We apply the decomposition to dual-pol data provided by Sentinel-1 (S1) covering different scenarios, such as agricultural, forest, urban, and glacial land-ice. We show that the polarized term of received polarization state is not usually the same as the transmitted one, and can therefore be used for key applications, e.g., classification and geo-physical parameter estimation. We show that, for vegetated terrain, depolarization is not the only influencing factor to S1 backscattered intensities and, in the case of vertical crops (e.g., rice), this allows the crop orientation effects to be decoupled from volume scattering in the canopy. We demonstrate that coherent dual-pol systems show strong phase signatures over glaciers, where the polarized contribution significantly affects the backscattered state, resulting in elliptical polarization on receive. This is a key result for S1, for which dual-pol phase analysis coupled to dense time series offer great opportunities for land-ice monitoring.

In this letter, we present a new algorithm for the detection of objects embedded in unwanted background signal. We employ systems in radar and optics capable of measuring the full polarimetric response via either the Mueller matrix or the complex scattering matrix. We first solve an optimization problem to find the maximum signal in the null subspace of the unwanted background. We then detail two levels of algorithm: the first restricted to backscatter systems (such as radar) when the dimensionality of the problem reduces, and also for the full bistatic general case suitable for optical analyses. We present examples to illustrate the application in radar ship detection and aerosol particle discrimination in optics.

In this letter, we present a new algorithm for the polarimetric calibration of spaceborne imaging radar systems. The technique combines two features: first, a new method for estimation of distortion matrices without the need for assumptions of small coupling or reciprocity; and second, it employs a factorized polar decomposition, which allows separate estimation of Faraday rotation from system distortion matrices in low-frequency spaceborne systems. We highlight a source of residual error in the procedure called hidden rotators, but show how to mitigate such errors to obtain fully calibrated data for arbitrary Faraday rotation. We illustrate using simulated data from the L-band ALOS-2 satellite.

In this letter, we present a new matrix formulation of range and azimuth ambiguities in polarimetric synthetic aperture radar. We consider the general hybrid case, where the scattering matrix is measured by transmitting a general orthogonal elliptical pair as interlaced pulse trains with simultaneous linear H and V receive. Earlier papers have shown some potential advantages for ambiguity suppression in this mode, and here, we confirm and expand these and show two key new ideas: first that successful suppression of range ambiguities is strongly phase dependent and second that suppression can be achieved for a set of elliptical polarizations along a great circle of the Poincaré sphere.

Compact polarimetry (CP) offers a tradeoff with fully polarimetric modes in terms of swath width, power budget, and polarimetric information content. In this letter, a classification comparison is made among real CP, simulated CP (SCP), and quad polarimetric (QP) data acquired from the L-band SAR system onboard the ALOS-2 satellite. The Wishart supervised classification scheme is used to compare data modes over two regions of a mixed test site in India. The quantitative classification assessment indicates that the QP data have higher classification accuracy than any other polarimetric combinations for both regions. The comparative classification accuracy of real versus SCP data is different for the two regions. The overall accuracy of the real CP data is slightly higher ~1% than SCP for region 1, which is dominated by urban and rice classes, whereas it is lower by ~9% for the agricultural crop dominated region 2.

Forest height is a key measurement for determining aboveground carbon in forests and forest biomass. DLR's TANDEM-X mission provides paired scenes that can be used for forest height measurement. We have demonstrated this in [1,2,3] for scenes with some moderate topography. This paper discusses issues for estimating forest canopy heights in mountainous terrain. Multiple dates and baselines of TanDEM-X data were obtained for a study site with much more severe topography and taller trees in BC, Canada. The results of analysis of large-slope induced errors and mitigation of some of these effects are discussed below.

In this paper we describe remote sensing methods with optical and radar sources to monitor aboveground carbon in forests. We move from forest/non-forest mapping, through forest type mapping (conifer, deciduous and mixed) to major species mapping. Biomass combined with species is required to compute the aboveground carbon. Biomass for forests needs accurate height measurements and allometric equations relating height, species and biomass. LIDAR data and TanDEM-X data were used for height measurement. We close with a discussion of accuracies and sources of error in making carbon maps with remote sensing and allometric models.

In this paper we report results of a study aimed at assessing the potential for using the standard mode of TanDEM-X (single polarization HH, single baseline and single-pass radar interferometry) to estimate forest canopy heights over two Canadian forest test sites, where supporting LIDAR data is available for validation. We first apply two coherence correction factors to TanDEM-X data, one for signal-to-noise ratio (SNR) based on noise statistics from TanDEM-X metadata and the other on a system correction based on bare surface reference scattering. We then use coherence amplitude of the interferogram and interferometric wavenumber kzto estimate forest canopy heights. The radar heights are validated against a LIDAR CHM (Canopy Height Model) reference. A simple linear scaling of the TanDEM-X height products based on a relatively small LIDAR calibration patch is used to extend the canopy height products over a mosaic of multiple swaths for very wide area forest height mapping using radar coherence.

In this study application results are presented derived from multi-parametric SAR observations covering five different thematic domains: forest, agriculture, ocean, urban and cryosphere. In total 21 application products have been selected and described. Their application on different data sets, space- and airborne sensors, was demonstrated and can independently be reproduced by any scientist. The results and algorithms are available soon through Springer.

Dual-pol HHVV images acquired by TerraSAR-X/TanDEM-X during 4 years, with 3 different incidence angles, over rice fields in Spain are analysed and employed to retrieve the phe-nological state of rice crops in a scale of 3 to 5 possible intervals. A decision tree approach is employed for the retrieval algorithm. Results show that the joint processing of all angles provides a good performance, only 2-4% worse than a separate processing. The role of polarimetry is demonstrated by obtaining a total accuracy of 88.6% when coherent dual-pol data are considered, instead of 75.6% for incoherent dual-pol.

The coherent nature of the acquisition by TerraSAR-X of both copolar channels (HH and VV) enables the generation of many different polarimetric observables with physical interpretation, as have recently been used for monitoring rice fields. In this letter, the influence of incidence angle upon these polarimetric observables is analyzed by comparing three stacks of images that were acquired simultaneously at different incidence angles (22°, 30°, and 40°) during a whole cultivation campaign. We show that the response of observables related to dominance (entropy, ratios of components) and type of scattering mechanisms (alpha angles) is not greatly influenced by incidence angle at some stages: early and advanced vegetative phases, and maturation. Moreover, the acquisition geometry drives the sensitivity to the presence of the initial stems and tillers, being detected earlier at shallower angles. This analysis is a necessary step before studying potential methodologies for combining different orbits and beams for reducing the time between acquisitions for monitoring purposes.

This paper gives results of our assessment of the potential for using Radarsat Constellation Mission (RCM) C-band compact polarimetry (compact-pol) for detecting historical forest fire scars. We first summarize the compact-pol decomposition theory we developed for retrieving useful geophysical parameters from compact-pol data. We then demonstrate a combination of time series filtering and spatial filtering to reduce speckle noise in the geophysical parameters. Next we describe a rule-based classifier and show an application example based on a time series of simulated compact-pol data from Radarsat-2 Fine Quad-pol (FQ) mode to detect a 10-year old fire scar in our study site. Our study results showed that even though there was a loss of polarimetric information through projection of a complex scattering matrix of quad-pol data on a single-pixel level, the compact-pol mode was capable of maintaining important polarimetric information and detecting the test forest fire scar. Finally we look at the effect of non-circular transmit polarization on key decomposition parameters and discuss the effects of imperfect transmit polarization on classification performance.

A set of ten RADARSAT-2 images acquired in fully polarimetric mode over a test site with rice fields in Seville, Spain, has been analyzed to extract the main features of the C-band radar backscatter as a function of rice phenology. After observing the evolutions versus phenology of different polarimetric observables and explaining their behavior in terms of scattering mechanisms present in the scene, a simple retrieval approach has been proposed. This algorithm is based on three polarimetric observables and provides estimates from a set of four relevant intervals of phenological stages. The validation against ground data, carried out at parcel level for a set of six stands and up to nine dates per stand, provides a 96% rate of coincidence. Moreover, an equivalent compact-pol retrieval algorithm has been also proposed and validated, providing the same performance at parcel level. In all cases, the inversion is carried out by exploiting a single satellite acquisition, without any other auxiliary information.

In this paper we report results of a study aimed at assessing the potential for using X-band polarimetric interferometry for forest height estimation in high latitude boreal and temperate rainforest environments. We first summarize the data sets available from the Tandem-X satellite pair for our two main test sites before deriving a new height estimation algorithm and finally comparing radar derived heights against reference lidar canopy height data for the two scenes.

In modern society, the anthropogenic influences on ecosystems are central points to understand the evolution of our planet. A polarimetric synthetic aperture radar may have a significant contribution in tackling problems concerning land use change, since such data are available with any-weather conditions. Additionally, the discrimination capability can be enhanced by the polarimetric analysis. Recently, an algorithm able to identify targets scattering an electromagnetic wave with any degree of polarization has been developed, which makes use of a vector rearrangement of the elements of the coherency matrix. In the present work, this target detector is modified to perform change detection between two polarimetric acquisitions, for land use monitoring purposes. Regarding the selection of the detector parameters, a physical rationale is followed, developing a new parameterization of the algebraic space where the detector is defined. As it will be illustrated in the following, this space is 6-D complex with restrictions due to the physical feasibility of the vectors. Specifically, a link between the detector parameters and the angle differences of the eigenvector model is obtained. Moreover, a dual polarimetric version of the change detector is developed, in case quad-polarimetric data are not available. With the purpose of testing the methodology, a variety of data sets were exploited: quad-polarimetric airborne data at L-band (E-SAR), quad-polarimetric satellite data at C-band (Radarsat-2), and dual-polarimetric satellite data at X-band (TerraSAR-X). The algorithm results show agreement with the available information about land changes. Moreover, a comparison with a known change detector based on the maximum likelihood ratio is presented, providing improvements in some conditions. The two methodologies differ in the analysis of the total amplitude of the backscattering, where the proposed algorithm does not take this into consideration.

In this paper we apply the ideas of target decomposition theory to compact polarimetry and use them to investigate forest applications at L-Band. We consider eigenvector methods (H/alpha), model based decompositions (RVOG) and coherent change detection (CCD) and show that compact modes are capable of maintaining important polarimetric information on forest structure and changes.

In this letter, we develop several new aspects of target decomposition theory for use with compact-mode polarimetric radar data. We first make a general link between fully polarimetric systems and compact modes before developing two important types of decomposition, namely, entropy/alpha and model-based surface/dihedral/volume techniques. We show that, under certain assumptions, compact data can be used to estimate the rotation invariant alpha angle of quadpol systems, which can then be used for polarimetric classification and physical parameter estimation. We apply the new methods to the problem of historical forest fire scar detection, using data at L- and C-bands to demonstrate the preservation of signatures in transition from quad to compact modes.

The feasibility of retrieving the phenological stage of rice fields at a particular date by employing coherent copolar dual-pol X-band radar images acquired by the TerraSAR-X sensor has been investigated in this paper. A set of polarimetric observables that can be derived from this data type has been studied by using a time series of images gathered during the whole cultivation period of rice. Among the analyzed parameters, besides backscattering coefficients and ratios, we have observed clear signatures in the correlation (in magnitude and phase) between channels in both the linear and Pauli bases, as well as in parameters provided by target decomposition techniques, like entropy and alpha from the eigenvector decomposition. A new model-based decomposition providing estimates of a random volume component plus a polarized contribution has been proposed and employed in interpreting the radar response of rice. By exploiting the signatures of these observables in terms of the phenology of rice, a simple approach to estimate the phenological stage from a single pass has been devised. This approach has been tested with the available data acquired over a site in Spain, where rice is cultivated, ensuring ground is flooded for the whole cultivation cycle, and sowing is carried out by randomly spreading the seeds on the flooded ground. Results are in good agreement with the available ground measurements despite some limitations that exist due to the reduced swath coverage of the dual-pol HHVV mode and the high noise floor of the TerraSAR-X system.

Target detectors using polarimetry are often focused on single targets, since these can be characterized in a simpler and deterministic way. The algorithm proposed in this paper is aimed at the more difficult problem of partial-target detection (i.e., targets with arbitrary degree of polarization). The authors have already proposed a single-target detector employing filters based on a geometrical perturbation. In order to enhance the algorithm to the detection of partial targets, a new vector formalism is introduced. The latter is similar to the one exploited for single targets but suitable for complete characterization of partial targets. A new feature vector is generated starting from the covariance matrix and exploited for the perturbation method. Validation against L-band fully polarimetric airborne E-SAR and ALOS PALSAR data and X-band dual-polarimetric TerraSAR-X data is provided with significant agreement with the expected results. Additionally, a comparison with the supervised Wishart classifier is presented revealing improvements.

In this paper we provide a review of developments in the field of polarimetric SAR interferometry or POLInSAR, beginning with its origins in data from the SIR-C mission in 1994 and culminating in its latest application to Tandem-X, a space-borne single pass X-Band interferometer launched in 2010. Linking these two important developments in technology we trace the parallel development of signal processing, coherent scattering models and bio-geophysical parameter estimation that have led us to our present understanding of the potential of this new technology for remote sensing applications.

This work presents a study about the evolution of the radar response of rice fields at X-band as a function of their phenology, by using three time series of coherent HHVV dual-pol X-band radar images acquired by the TerraSAR-X sensor at different incidence angles during a whole cultivation season. After analyzing a wide set of polarimetric observables, with particular sensitivities at different moments along the campaign, an approach to retrieve the phenological stage with a single acquisition is proposed and tested.

Several POLInSAR methodologies are based on the concept of polarimetric and interferometric coherence [1]. Many of them assume the scattering mechanisms observed at the two interferometric acquisitions are the same (i.e. equi-scattering mechanism, ESM). However, if the scatterers change, different approaches must be employed. In order to select the appropriate set of algorithms, a test of target stability should be performed as pre-processing. The authors already devel oped a polarimetric change detector [2], based on perturbation analysis. In the present paper, the change detector and the parameterization introduced in [2] are modified in order to perform the ESM test on dual polarimetric TanDEM-X data. Unfortunately, TanDEM-X data were not available at the mo ment of compiling this paper, so the algorithm was tested on TerraSAR-X HHIVV images.

In this paper, biomass estimations are compared using quadpol ALOS PALSAR, TerraSAR-X and LIDAR data over Glen Affric, Scotland. Biomass was retrieved from the ALOS PALSAR and TerraSAR-X data using Yamaguchi decomposition to obtain useful information about the scattering mechanisms. A regression equation is obtained from the establishment of a relationship between the ratio of volume and surface scattering mechanism and the biomass obtained from fieldwork. Since the study site is a mountainous region, the terrain slope effects need to be compensated before retrieving the biomass. For LIDAR, the vertical structure of both the underlying topography and the forest structure were generated to estimate the biomass allometrically. Validation on the results of the biomass estimation was done by comparing the biomass estimated using ALOS PALSAR, TerraSAR-X and LIDAR. The results suggest that in some areas the biomass retrievals are broadly comparable.

Target detectors using polarimetry are often focused on single (coherent) targets, since these are the ones that can be more simply characterized polarimetrically. The new proposed algorithm is aimed at the more difficult problem of partial target detection (i.e. targets with any degree of polarization). A new feature vector is defined starting from the coherency matrix, and then a perturbation method is performed. Starting from the partial target detection, a novel classification algorithm is proposed. The validation is carried out against fully polarimetric satellite data. In particular, X band TerraSAR-X and L band ALOS PALSAR are employed, providing significant agreement with the expected results and the supervised Wishart classifier.

The contribution of synthetic aperture radar polarimetry in target detection is described and found to add valuable information. A new target detection methodology that makes novel use of the polarization fork of the target is described. The detector is based on a correlation procedure in the target space, and other target representations (e.g., Huynen parameters or ¿ angle) can be employed. The mathematical formulation is general and can be applied to any kind of single target; however, in this paper, the detection is optimized for the odd and even bounces (the first two elements of the Pauli scattering vector) and for the oriented dipoles. Validation against real data shows significant agreement with the expected results based on the theoretical description.

This paper deals with a new class of localized point targets we call polarized point scatterers (PPS). They can be detected from Quadpol SAR data by using a two-dimensional filter, employing an eigenvalue/alpha threshold. They can then be used to estimate the dielectric constant of the target using a simple scattering model. We first consider the basic performance of the filter based on selection of thresholds and data window size to keep false alarms to a desired low level. We then illustrate application of the filter to sample data sets from the ALOS-PALSAR archive, showing its important application to urban areas.

In this paper we combine a macro-ecology forest model with a numerical scattering code to estimate the vertical scattering profile of L-band penetration into forests. We use these simulations to show that structure has an important impact on interferometric coherence and hence on height retrievals based on single baseline polarimetric interferometry. We conclude with some suggestions for dual baseline extensions to allow for better height and structure estimation in future sensors.

In this paper we investigate the advantages of using full quadpol low frequency radar imaging data for large area land-use classification and forest biomass estimation. We employ multiple PALSAR data sets over test sites in Malawi, Africa, where we have extensive in-situ measurements and ground campaigns for validation. We show how L-band PLR modes show great potential for quantitative land use applications and important sensitivity to above ground biomass.

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.

In this paper we introduce a new algorithm for radar estimation of the vertical structure of vegetated land surfaces. The algorithm is based on a low frequency 3-D image reconstruction using polarization coherence tomography or PCT. We first summarize the technique, present a new method for selecting the optimum interferometric baseline and then apply it to airborne P and X-Band data from the DLR E-SAR system. We conclude by assessing the potential of the technique for providing improved global and regional forestry products using space-borne radar remote sensing.

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.

In this letter, we consider a dual-baseline formulation of coherence tomography and show how practical application of the method is limited by numerical stability. To help reduce this, we propose a regularization technique based on a matrix singular value decomposition to stabilize the inversion. We then apply the new dual-baseline algorithm to ground-based radar data from the European Microwave Signature Laboratory. We consider a sample of maize plants and employ dual-baseline interferometric data to reconstruct vertical tomograms through the vegetation as a function of frequency. We use these reconstructions to interpret the primary scattering mechanisms and their polarization dependence

The application of polarimetric SAR interferometry technology is extended to ground based radar imaging (GB-POLInSAR), which has a limited field of view but provides wide band coverage. It also provides for easier deployment of broadband and multi-baseline techniques, from which we can estimate vegetation structure and extinction propagation using model based techniques. This micro-scale multi-parameter combination with good temporal resolution is a unique feature of ground- based sensors. In this paper we present both polarimetric and interferometric coherence calibration results for such a GB-POLInSAR system we have developed at the University of Adelaide. We then show an initial study of height estimation of Australian native plants based on the coherence parameter retrieval models of the polarimetric SAR interferometry technique using the broadband GB-POLInSAR system.

In this paper we present an analysis of the potential for \nimplementing polarimetric SAR Interferometry (POLInSAR) \nwith the new L band phased array SAR sensor (PALSAR) to be \nlaunched on board the JAXA-ALOS satellite. We first summarise \na new system design concept, the phase tube, we have developed \nfor general POLInSAR assessment and then present a detailed \nanalysis of the dual and quadpol modes of ALOS-PALSAR.

ESA-ESRIN 2010 (PolSARpro Toolbox: Maintenance and Continuation)

In this letter we develop a new concept, the negative alpha filter, which we suggest has application for quantitative estimation of surface parameters beneath vegetation using polarimetric synthetic aperture radar (SAR) interferometry (POLInSAR). We first derive the filter and then validate it using simulations of L-band coherent forest scattering. We then show initial results of applying the filter to airborne data from the German Aerospace Center's E-SAR L-band sensor.

The performance of quantitative remote sensing based on multidimensional synthetic aperture radars (SARs), and polarimetric SAR systems in particular, depends strongly on a correct statistical characterization of the data, i.e., on a complete knowledge of the effects of the speckle noise. In this framework, the eigendecomposition of the covariance or coherency matrices and the associated H//spl alpha/_/A decomposition have demonstrated the potential for quantitative estimation of physical parameters. In this paper, we present a detailed study of the statistics associated with this decomposition. This analysis requires the introduction of mathematical tools that are not well known in the remote sensing community. For this reason, we include a review section to present them. Using this work, we then present an expression for the probability density function of the sample eigenvalues of the covariance or coherency matrix. The availability of this expression allows a complete study of the separated sample eigenvalues, as well as, the entropy H and the anisotropy A. As demonstrated, all these parameters must be considered as asymptotically nonbiased with respect to the number of looks. In order to reduce the biases for a small number of averaged samples, a novel estimator for the eigenvalues is proposed. The results of this work are analyzed by means of simulated and real airborne SAR data. This analysis permits us to determine in detail the effects of the number of averaged samples in the estimation of physical information in radar polarimetry.

The quantitative bio/geo physical agricultural and land-use parameter estimation has attained significant prominence as it is a challenge to quantify complex and fast changing media in terms of geometry and material properties. In this work the behavior of agricultural vegetation in terms of polarimetric interferometric SAR (Pol-InSAR) observables is investigated. For this a two layer model, the Oriented Volume over Ground (OVoG) model, is discussed and validated against experimental data

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.

In this paper we develop a new algorithm for improved tree height retrieval based on dual baseline polarimetric SAR interferometry. We test the algorithm using coherent simulations of forest canopy scattering.

In this work the potential of using the interferometric coherence at different polarization over surface scatterers to extract information about surface parameters is investigated. The sensitivity of the individual coherence contributions to surface roughness and moisture conditions is discussed and simulated using a novel hybrid polarimetric surface scattering model. The model itself consists of a coherent part obtained from the extended Bragg model and incoherent part obtained from the integral equation model. Finally, the experimental airborne SAR data are used to validate the proposed model at different polarizations.

In this paper, we propose a new speckle filter for polarimetric SAR that preserves pixels' scattering properties. The basic principle is to select pixels of the same scattering characteristics to be included in the average. To achieve this, the algorithm first applies the Freeman and Durden decomposition to separate pixels into three scattering categories: surface, double bounce and volume, and then unsupervised classification is applied. Speckle filtering is performed using the classification map masks. Pixels centered in a 9/spl times/9 window are filtered by including only pixels in the same and two neighboring classes from the same scattering category. This algorithm is extended to the coherence estimation of polarimetric interferometry SAR data. The effectiveness of this new algorithm is demonstrated with JPL AIRSAR and E-SAR data.

In this paper we develop a simple model for the polarimetric radar backscatter from breaking ocean waves. We show that the dynamics of the breaking process lead to characteristic variations in the wave depolarization processes. We compare the model prediction with X-band grazing incidence radar data collected for breaking ocean waves in the surf zone.

In this paper, a technique based on a non-orthogonal joint diagonalization algorithm to separate the vegetation and the ground components from Polarimetric Synthetic Aperture Radar Interferometry (POL-InSAR) measurements is proposed. Non- orthogonal joint diagonalization is a technique used to separate sources, which may not be orthogonal to each other. In this approach, we jointly diagonalize the covariance matrices and show that the non-orthogonal eigenvectors obtained by this method represent the ground and the vegetation scattering components.

This paper addresses the effect of temporal decorrelation on the inversion of forest parameters using Pol- InSAR techniques. The modeling of temporal decorrelation and the inversion of single-baseline Pol-InSAR data in the presence of temporal decorrelation is discussed. Model based simulations and experimental multi-temporal fully polarimetric and repeat pass interferometric data from the SIR-C Space shuttle mission are used for the performance analysis of the proposed approach. Keywords-component: Polarimetric SAR interferometry (Pol- InSAR), Forest Parameter Estimation.

Proposes a new model for the inversion of surface roughness and soil moisture from polarimetric synthetic aperture radar (SAR) data, based on the eigenvalues and eigenvectors of the polarimetric coherency matrix. It demonstrates how three polarimetric parameters, namely the scattering entropy (H), the scattering anisotropy (A), and the alpha angle (/spl alpha/) may be used in order to decouple surface roughness from moisture content estimation offering the possibility of a straightforward inversion of these two surface parameters. The potential of the proposed inversion algorithm is investigated using fully polarimetric laboratory measurements as well as airborne L-band SAR data and ground measurements from two different test sites in Germany, the Elbe-Auen site and the Weiherbach site.

Recently, polarimetric synthetic aperture radar (SAR) interferometry has generated much interest for forest applications. Forest heights and ground topography can be extracted based on interferometric coherence using a random volume over ground coherent mixture model. The coherence estimation is of paramount importance for the accuracy of forest height estimation. The coherence (or correlation coefficient) is a statistical average of neighboring pixels of similar scattering characteristics. The commonly used algorithm is the boxcar filter, which has the deficiency of indiscriminate averaging of neighboring pixels. The result is that coherence values are lower than they should be. In this paper, we propose a new algorithm to improve the accuracy in the coherence estimation based on speckle filtering of the 6/spl times/6 polarimetric interferometry matrix. Simulated images are used to verify the effectiveness of this adaptive algorithm. German Aerospace Center (DLR) L-Band E-SAR data are applied to demonstrate the improved accuracy in coherence and in forest height estimation.

Fully polarimetric SAR data analysis has found wide application for terrain classification, land-use, soil moisture and ground cover classification. New methods and algorithms continue to be developed and tested. One method that has gained great popularity is the Cloude-Pottier eigenvalue/eigenvector decomposition of coherency matrices (Cloude and Pottier 1996). The eigenvalue spectrum uniquely describes the scattering entropy, anisotropy and span (total power). The eigenvectors are employed in the calculation of averaged quantities. Typically though the eigenvector components have not been analyzed in much detail. Here we present a general analysis of the eigenvectors based on an earlier observation (Cloude 2001) that the eigenvectors may be parameterized by a set of rotations.

Radar Polarimetry, Radar Interferometry and Polarimetric SAR Interferometry represent the current culmination in 'Microwave Remote Sensing' technology, but we still need to progress very considerably in order to reach the limits of physical realizability. Whereas with radar polarimetry the textural fine-structure, target orientation, symmetries and material constituents can be recovered with considerable improvement above that of standard 'amplitude-only' radar; by implementing 'radar interferometry' the spatial (in depth) structure can be explored. With Polarimetric Interferometric Synthetic Aperture Radar (POL-IN-SAR) imaging, it is possible to recover such co-registered textural and spatial information from POL-IN-SAR digital image data sets simultaneously, including the extraction of Digital Elevation Maps (DEM) from either Polarimetric (scattering matrix) or Interferometric (dual antenna) SAR systems. Simultaneous Polarimetric-plus-Interferometric SAR Imaging offers the additional benefit of obtaining co-registered textural-plus-spatial three-dimensional POL-IN-DEM information, which when applied to Repeat-Pass Image-Overlay Interferometry provides differential background validation and environmental stress-change information with highly improved accuracy. Then, by either designing multiple dual polarization antenna POL-IN-SAR systems or by applying advanced POL-IN-SAR image compression techniques, will result in 'POL-arimetric TOMO-graphic' (Multi-Interferometric) SAR or POL-TOMO-SAR imaging.

In this paper we investigate the use of radar polarimetry and polarimetric radar interferometry for measuring the properties of random chiral particle clouds. Helicity is an established concept in radar polarimetry but to date it has been restricted to coherent scattering mechanisms. Here we show how it can be used for random media studies and conclude as to its potential impact in the radar remote sensing of vegetation.

In this paper we employ a two-level polarimetric coherent vegetation scattering model to investigate parameter estimation for dual baseline fully polarimetric radar interferometry. We show that the height and ground phase can be determined without assumptions about the minimum ground to volume scattering ratio and also we investigate the influence of vertical canopy structure on inversion.

In this paper we introduce a new ratio of polarimetric scattering coefficients that we suggest is sensitive to surface moisture content and insensitive to roughness variations. The parameter is first derived from the extended Bragg surface model and then validated using wide band chamber measurements of surface backscatter collected at the European Microwave Scattering Laboratory (EMSL).

In this work the potential of using the interferometric coherence at different polarisations over surface scatterers in order to extract information about surface parameters is investigated. The sensitivity of the individual coherence contributions to surface roughness and moisture conditions is discussed and simulated using a polarimetric surface scattering model. Finally, experimental airborne SAR data are used to demonstrate the discussed effects.

This paper develops a technique for separating the ground and vegetation components from polarimetric interferometric SAR measurements. The objective is to use this technique to remove the vegetation bias in interferometric measurements of elevation in forested terrain. Initial results indicate that the technique is able to compensate for the vegetation bias and provide improved estimates of the terrain elevation. However, further work is needed to estimate geophysical information about the vegetation canopy.

Polarimetric SAR interferometry has generated much interest for forest applications. In this paper, we study problems associated with interferometric coherence estimation and propose two algorithms to improve the accuracy in the coherence estimation based on speckle filtering of the 6 /spl times/ 6 polarimetric interferometry matrix.

Recently, several passive interferometric configurations consisting of two or more receiving only micro-satellites operating in a fully polarimetric mode positioned ahead or behind a master satellite have been proposed as a cost efficient option for future single-pass interferometric spaceborne SAR systems. In this paper the potential performance of such a passive polarimetric and interferometric micro-satellite concept for the estimation of forest structure parameters in terms of polarimetric interferometry is investigated.

In this paper we summarise the recent results from the Glen Affric radar project, a multi-disciplinary program addressing the potential of polarimetric radar interferometry to provide vegetation structural information of importance in forest mapping and ecology studies. We present a comparison of results from L-band repeat pass SAR imagery with detailed in-situ measurements of forest height and topography.

Examines the application of single-baseline polarimetric SAR interferometry to the remote sensing and measurement of structure over forested terrain. For this, a polarimetric coherent scattering model for vegetation cover suitable for the estimation of forest parameters from interferometric observables is introduced, discussed and validated. Based on this model, an inversion algorithm which allows the estimation of forest parameters such as tree height, average extinction, and underlying topography from single-baseline fully polarimetric interferometric data is addressed. The performance of the inversion algorithm is demonstrated using fully polarimetric single baseline experimental data acquired by DLR's E-SAR system at L-band.

The authors show how the entropy-alpha target decomposition scheme may be used for parametric inversion studies on random particle cloud models for vegetation layers. The decomposition is detailed first and then applied to a two-parameter model for backscatter from a random cloud of small anisotropic particles. The two main parameters used are the mean particle shape and the mean orientation angle of the cloud. An inversion algorithm is presented and applied to broad-band polarimetric radar data from the European Microwave Signature Laboratory (EMSL), Joint Research Center, Ispra, Italy. The results have been obtained from measurements of a fir tree and a ficus tree. They show a wavelength scale dependence of the shape and distribution of scatterers, which reflects the complex volume scattering nature of such problems. Moreover, the values and trends from these two trees as a function of the frequency are different, as expected from their physical structures. Consequently, this algorithm has the potential to be useful in the construction of classification schemes for vegetation.

The authors propose a new method for unsupervised classification of terrain types and man-made objects using polarimetric synthetic aperture radar (SAR) data. This technique is a combination of the unsupervised classification based on polarimetric target decomposition, S.R. Cloude et al. (1997), and the maximum likelihood classifier based on the complex Wishart distribution for the polarimetric covariance matrix, J.S. Lee et al. (1994). The authors use Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The classified results are then used to define training sets for the next iteration. Significant improvement has been observed in iteration. The iteration ends when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. The authors observed that the class centers in the entropy-alpha plane are shifted by each iteration. The final class centers in the entropy-alpha plane are useful for class identification by the scattering mechanism associated with each zone. The advantages of this method are the automated classification, and the interpretation of each class based on scattering mechanism. The effectiveness of this algorithm is demonstrated using a JPL/AIRSAR polarimetric SAR image.

Polarimetric radar interferometry is much more sensitive to the distribution of oriented objects in a vegetated land surface than either polarimetry or interferometry alone. This paper shows that single-baseline polarimetric interferometry can be used to estimate the heights of oriented-vegetation volumes and underlying topography, while at least two baselines are needed for randomly oriented volumes. Single-baseline, calculated vegetation-height accuracies are in the range of 2-8 m for reasonable levels of vegetation orientation in forest canopies.

The authors examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. They first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, they show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows them to reveal the strong polarization dependency of the interferometric coherence. They then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, they demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. They investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/X-SAR data collected during October 8-9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.

The authors outline a new scheme for parameterizing polarimetric scattering problems, which has application in the quantitative analysis of polarimetric SAR data. The method relies on an eigenvalue analysis of the coherency matrix and employs a three-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters from the data. The scattering entropy is a key parameter is determining the randomness in this model and is seen as a fundamental parameter in assessing the importance of polarimetry in remote sensing problems. The authors show application of the method to some important classical random media scattering problems and apply it to POLSAR data from the NASA/JPL AIRSAR data base.

In this paper, we provide a review of the different approaches used for target decomposition theory in radar polarimetry. We classify three main types of theorem; those based on the Mueller matrix and Stokes vector, those using an eigenvector analysis of the covariance or coherency matrix, and those employing coherent decomposition of the scattering matrix. We unify the formulation of these different approaches using transformation theory and an eigenvector analysis. We show how special forms of these decompositions apply for the important case of backscatter from terrain with generic symmetries.

We outline a Fourier based algorithm for application to SAR image formation using ultra wide band (UWB) radar signals. We illustrate application of the processor to simulated imagery based on backscatter from canonical targets such as a sphere, dipole and flat plate. The predictions are made directly in the time domain using a finite difference time domain scattering code. We illustrate the effect of antenna distortions on the quality of the imagery obtained and show that important image distortions can arise due to the frequency dependence of the backscattered signal.>

By using the first-order iterative solution of vector radiative transfer in small albedo for a layer of nonuniformly-oriented, random, nonspherical scatterers, the Mueller matrix and coherency matrix are obtained. Eigenanalysis of the coherency matrix is numerically calculated. The eigenanalysis of the coherency matrix is proposed as a better method for identifying physical scattering mechanisms than direct inspection of the Mueller matrix. The functional dependence of polarimetric scattering is also discussed.>