In this paper, we will present a determinant ratio test (DRT) statistic to measure the similarity of two covariance matrices for unsupervised change detection in polarimetric radar images. The multilook complex covariance matrix is assumed to follow a scaled complex Wishart distribution. In doing so, the distribution of the DRT statistic is analytically derived which is exactly Wilks’s lambda of the second kind distribution, with density expressed in terms of Meijer G-functions. Due to this distribution, the constant false alarm rate (CFAR) algorithm is derived in order to achieve the required performance. More specifically, a threshold is provided by the CFAR to apply to the DRT statistic producing a binary change map. Finally, simulated and real multilook polarimetric radar data are employed to assess the performance of the method and is compared with the Hotelling–Lawley trace (HLT) statistic.

This paper proposes a new method for automatic change detection in multilook polarimetric synthetic aperture radar (PolSAR) images based on unsupervised classification of these data. Changes are identified by comparing multiple classification images. A Bayesian Nonparametric (BNP) approach such as the Dirichlet process mixture model (DPMM) is a potential method for classification tasks. It provides a framework for estimating both the number of components in a mixture model and the parameters of the individual mixture components simultaneously from PolSAR data. Usually, DPMM is treated using Markov chain Monte Carlo (MCMC) or variational Bayes (VB) methods. Here, we propose an expectation-maximization (EM) algorithm to estimate the parameters of the DPMM for high resolution PolSAR images which are modeled by the product model. The performance of the proposed method is evaluated with PolSAR images and the preliminary results on classification and finally on change detection are satisfactory and meet our expectations.

This paper presents the first comparisons of backscattering coefficients σ0between acquisitions from the SWALIS (Still WAter Low Incidence Scattering) airborne sensor and the space mission SWOT (Surface Water and Ocean Topography) over the Mont Saint-Michel (MSM) bay and over the Gondrexange and Stoke ponds. The MSM area is chosen both as a calibration/validation area for the SWOT mission and as a perfect area for analyzing the backscattering of specific sand areas.

The orthogonal frequency-division multiplexing (OFDM) is a promising waveform for joint radar and communication systems. In this article, a unified approach is proposed to analyse both radar and communication robustness with respect to Doppler mismatch. The analytical study leads to an expression that links the radar performance to the communication one. The performance of a delay-Doppler radar is quantified by modelling the delay response of the OFDM radar with an appropriate random variable. Different delay filters are analysed, among which the Wiener filter. The analytical results are validated by simulations, they show that the radar system benefits from the Wiener filter, and the configurations where this filter outperforms the other ones are outlined.

In this article, we propose a determinant ratio test (DRT) statistic to measure the similarity of two covariance matrices for unsupervised change detection in polarimetric radar images. The multilook complex covariance matrix is assumed to follow a scaled complex Wishart distribution. In doing so, we provide the distribution of the DRT statistic that is exactly Wilks’s lambda of the second kind distribution, with density expressed in terms of Meijer G-functions. Due to this distribution, the constant false alarm rate (CFAR) algorithm is derived in order to achieve the required performance. More specifically, a threshold is provided by the CFAR to apply to the DRT statistic producing a binary change map. Finally, simulated and real multilook polarimetric SAR (PolSAR) data are employed to assess the performance of the method and is compared with the Hotelling–Lawley trace (HLT) statistic and the likelihood ratio test (LRT) statistic.

In this paper, we propose a new method for automatic change detection in multi-temporal fully polarimetric synthetic aperture radar (PolSAR) images based on multivariate statistical wavelet subband modeling. The proposed method allows us to take into account the correlation structure between subbands by modeling the wavelet coefficients through multi-variate probability distributions. Three types of correlation are investigated: inter-scale, inter-orientation, and inter-polarization dependences. The multivariate generalized Gaussian distribution (MGGD) is used to model the interdependencies between wavelet coefficients at different orientations, scales, and polarizations. Kullback-Leibler similarity measures are computed and used to generate the change map. Simulated and real multilook PolSAR data are employed to assess the performance of the method and are compared to the multivariate Gaussian distribution (MGD) based method. We show that the information embedded in the correlation between subbands improves the accuracy of the change map, leading to better performance. Moreover, the MGGD represents better the correlations between wavelet coefficients and outperforms the MGD.

In this article, we propose an efficient heterogeneous change detection algorithm based on stochastic distance measure between two Gd0distributions. Due to its flexibility and simplicity, the matrix-variate Gd0distribution has been successfully used to model the multilook polarimetric synthetic aperture radar (PolSAR) data and has been tested for classification, segmentation, and image analysis. Concretely, closed-form expressions for the Kullback-Leibler, Rényi of order β, Bhattacharyya, and Hellinger distances are provided to compute the stochastic distance between Gd0distributions. In this context, we resort to the expectation-maximization (EM) to estimate accurately with low complexity the parameters of the probability distribution of the two multilook polarimetric covariance matrices to be compared. Finally, the performance of the method is compared firstly to the performance of other known distributions, such as the scaled complex Wishart distribution, and secondly to other known statistical tests using simulated and real multilook PolSAR data.

The product model is assumed to be an appropriate statistical model for multilook polarimetric synthetic radar data (PolSAR). According to this model, the observed signal is considered as the product of independent random variates of a complex Gaussian speckle and a non-Gaussian texture. With different texture distributions, the product model leads to different expressions for the compound distribution considered as an infinite mixture model. In this paper, the maximum-likeli-hood (ML) estimator is derived to jointly estimate the speckle and texture parameters in the compound distribution model using the multilook polarimetric radar data. In particular, we estimate: 1) the equivalent number of looks; 2) the covariance matrix of the speckle component; and 3) the texture distribution parameters. The expectation-maximization algorithm is developed to compute the ML estimates of the unknown parameters. The hybrid Cramer-Rao bounds (HCRBs) are also derived for these parameters. First, a general HCRB expression is derived under an arbitrary texture distribution. Then, this expression is simplified for a specific texture distribution. The performance of the ML is compared with the performance of other known estimators using the simulated and real multilook PolSAR data. For real data, a goodness of fit of multilook PolSAR data histograms is used to assess the fitting accuracy of the compound distributions using different estimators.

This paper proposes a new unsupervised image segmentation method for multilook polarimetric synthetic aperture radar (PolSAR) data. The statistical model for the PolSAR data is considered as a finite mixture of non-Gaussian compound distributions considered as the product of two statistically independent random variates, speckle, and texture. With different texture distributions, the product model leads to various expressions of the compound distribution. The method uses a Markov random field (MRF) model for pixel class labels. The expectation-maximization/maximization of the posterior marginals (EM/MPM) algorithm is used for the simultaneous estimation of texture and speckle parameters and for the segmentation of multilook PolSAR images. Simulated and real PolSAR data are shown to demonstrate the method.

This letter presents the use of polynomial Wigner-Ville distribution (PWVD) for accurate velocity estimation as used in remote road traffic management applications. In such applications based on inverse synthetic aperture radar, velocity estimation is central to obtain a suitable level of performance of the signal processing. Moreover, the precision of this velocity estimation is crucial in order to achieve the best detection and estimation of the gauge of the vehicles through the use of radar images. Hence, the PWVD is applied as an instantaneous frequency estimator used in this traffic surveillance application.

This paper presents an opportunity to cancel range ambiguities in synthetic aperture radar (SAR) configuration. One of the limitations of SAR systems is the range ambiguity phenomenon that appears with long delayed echoes. The reflected signal corresponding to one pulse is detected when the radar has already transmitted the next pulse. Thus, this signal is considered as an echo from the next pulse. This paper investigates the opportunity of coding the transmitted pulses using an orthogonal frequency-division multiplexing pulse. The results show that coded-OFDM signals outperform conventional chirp signal and make it possible to relax constraints placed upon the pulse repetition frequency.

The project entitled Evaluation of RADARSAT-2 quad-pol data for functional assessment of wetlands (Id6842), developed in the framework of the CSA-ESA SOAR-EU (Science and operational applications research for Europe) aims to contribute to the application development in demonstrating the exploitation of fully polarimetric time-series datasets for the functional assessment of wetlands. The objective of this paper is to address the issue of evaluating fully polarimetric RADARSAT-2 time-series datasets to determine the water cycle dynamics, in order to delineate precisely potential, effective and efficient wetlands.

Remote sensing applications need specific signal processing to obtain high resolution for radar images. One of limitations for synthetic aperture radar (SAR) systems is the range ambiguity phenomenon that appears with long delayed echoes. The reflected signal corresponding to the pulse (n) is detected when the radar has already transmitted the next pulse (n + 1). Thus, this signal is considered as an echo from the pulse (n + 1). This paper investigates the opportunity of coding the transmitted pulse using an orthogonal frequency division multiplexing (OFDM) pulse. The results show that coded OFDM signal outperform conventional chirp signal.

The objective of this study is to evaluate the potential of C-band polarimetric multi-angular Radarsat-2 datasets to characterize soil moisture and surface roughness over bare agricultural fields. The polarimetric parameters derived from single angular and dual angular SAR data are examined to analyze the polarimetric sensitivities to bare surfaces. In the case of single angular analysis, the results show that smooth surfaces can be separated from medium rough as well as rough surfaces. Polarimetric parameters SRalpha1, ρhhvvhave potentials to characterize soil moisture. In the case of dual angular analysis, the backscattering coefficient differences between two incidence angles Δσ in HH and VV polarization depend negatively on surface roughness and positively on soil moisture.

In order to reduce the cost of imaging systems, the use of a switch matrix instead of the standard multichannel schema is considered to be a good solution. However, the design of a switch matrix for imaging applications is a complex task, particularly for a 2-D array because it depends mainly on the array topology, the number of channels, and the required performance. In this letter, a switch submatrix approach for a passive imaging system is presented and discussed. The goal is to reduce the complexity and the cost of the system architecture while maintaining its high performance. An optimization method that is able to yield optimal sparse arrays with a very low redundancy and an optimized switching process is then proposed. Numerical results confirm the efficiency of the proposed method. A good tradeoff between cost, performance, and system complexity is also reported.

Remotely sensed data are widely used to identify, delineate and characterize wetlands. Optical data provide interesting information on land-use and land cover but are limited to cloud-free periods and to a description of the top layer of the vegetation strata because penetration depth is very small. For these reasons, it is not possible to precisely inventory wetland vegetation and agricultural practices, as well as water cycles and water levels in these areas with passive remote sensing techniques. The objective of this article is to evaluate fully polarimetric RADARSAT-2 time series datasets to determine the water cycle dynamics, in order to delineate precisely potential, effective and efficient wetlands. To that end, the development and validation of a supervised PolSAR segmentation including multi-temporal analysis of wetland evolution, and the investigation of polarimetric decomposition methods for quantitative physical parameter inversion algorithms are presented. The proposed methodology is based on the segmentation of a polarimetric descriptor, the Shannon Entropy, which has been shown to be a very sensitive parameter to the temporal variability of flooded areas. The results have been validated using soil moisture measurements in the field and a LiDAR image. They show that it is possible to produce detailed water feature maps useful for delineating water tables as well as water-saturated areas, and for monitoring water area dynamics. These products provide useful information to identify and delineate wetlands in order to support conservation and management in these ecosystems across large areas.

The most intuitive way to extract depth information from remote sensing images is stereogrammetry, in which a digital elevation model (DEM) is achieved by computing stereoscopic radar images. When only the amplitude of the radar images is considered, this computation is called radargrammetry. The main idea of which is to match stereopair radar images in order to create a disparity map from one image to the other and, finally, to compute the elevation. Therein, we present our studies on the extraction of 3-D information from radar images. We examine a way to produce a DEM of a challenging area of the French Alps. The central issue of this paper concerns improvements for radargrammetric synthetic aperture radar image processing for high-relief reconstruction, and we focus on the matching step, which is one of the most important points of the radargrammetric processing. Thus, we propose original methods using different correlation windows. On the one hand, we take the advantages of a multiwindow approach to combine relevant information by multiplying the correlation surfaces obtained for each correlation window size during the matching operation. On the other hand, the second improvement is based on the expansion of windows on foreshortened areas, particularly because of the side-looking radar view. These methods allow us to achieve reliable image matching and to improve the accuracy of the DEM.

This paper deals with the relevance of using stereoscopic radar images in order to retrieve the relief of terrain. Firstly, the basic characteristics of the radargrammetry are described. Thus, we present the results of the radargrammetric processing using the ZNCC procedure and applied to radar images. These images are recorded by the SIR-C mission over the French Alps. The results show that the image matching can fail, especially in foreshortened areas. So, we expose two different improvement methods using several correlation windows in order to cancel the reconstruction errors. The first improvement take advantage of a multi-window approach to combine information multiplying the correlation surfaces obtained for each correlation window size. The second improvement is based on another multi-window approach that makes it possible to get correlation windows adapted to the foreshortened areas. Finally, we combine these two improvement methods to show that it's possible to make the disparity map more reliable for the first step of the pyramidal scheme.

ISBN: 978-1-4244-9566-5 - WOS

The objective of this paper is to make a review of the current status of the project entitled Evaluation of RADARSAT-2 quad-pol data for functional assessment of wetlands (Id6842), developed in the frame of the CSA-ESA SOAR-EU (Science and operational applications research for Europe) program by a consortium comprising I.E.T.R at the University of Rennes 1 and COSTEL-LETG at the University of Haute-Bretagne. The main objective of this project concerns in evaluating fully polarimetric RADARSAT-2 time-series datasets to delineate precisely effective and potential wetlands, map detailed vegetation distribution, identify agricultural practices and determine water cycle and waterlevels.

The aim of this paper is to present our studies about extraction of 3D information from radar images. Several radargrammetric methods allow DEM (digital elevation model) generation from SAR images and we take a special interest to stereoscopic method. The main idea is to match image stereo pairs, to create a disparity map from one image to the other and to compute elevation thanks to the incidences angles.