Multilook Polarimetric Synthetic Aperture Radar Data Research Articles

Multivariate Statistical Modeling for Multitemporal SAR Change Detection Using Wavelet Transforms and Integrating Subband Dependencies

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.

A family of divergence-based classifiers for Polarimetric Synthetic Aperture Radar (PolSAR) imagery vector and matrix features

ABSTRACT Polarimetric Synthetic Aperture Radar (PolSAR) is one of the most important remote sensing tools. However, PolSAR images are strongly contaminated by a multidimensional interference (called speckle noise), making their processing (e.g. in the classification context) difficult. In terms of structure, multilook PolSAR data follow a definite positive hermitian behaviour and, therefore, require tailored classifiers for their features. Some classic classifiers – such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbours (KNN), and Support Vector Machine (SVM) – have been yielding unacceptable performance to these data, when applied directly. One justification is because they do not often take into account neither the speckle presence nor properties which are inherent to under-study relieves. This paper addresses a collection of PolSAR divergence-based classifiers, deduced from the normal, skew-normal, t-Student, and skew-t vector models as well as the scaled complex Wishart (SCW) distribution. The last model is a standard supposition to describe multilook PolSAR data, having two parameters: covariance matrix (which is directed to data nature) and number of looks (which controls the speckle noise effect). The considered remainder laws aim to model the main diagonal of these data, known as multivariate intensities. The performance of proposed methods is quantified and compared with those due to the Kullback-Leibler (KL) distance for multivariate normal distribution and to LDA, QDA, KNN, and SVM methods. Experiments with both artificial and real PolSAR data are considered. Results favour optimal Rényi classifiers for an Airborne Synthetic Aperture Radar (AIRSAR) image of San Francisco and the t-Student KL classifier for an SAR image system of the Electromagnetics Institute (EMISAR) image of Foulum.

Multilook Polarimetric SAR Change Detection Using Stochastic Distances Between Matrix-Variate Gd 0 Distributions

In this article, we propose an efficient heterogeneous change detection algorithm based on stochastic distance measure between two $\mathcal {G}_{d}^{0}$ distributions. Due to its flexibility and simplicity, the matrix-variate $\mathcal {G}_{d}^{0}$ distribution 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, Renyi of order $\beta $ , Bhattacharyya, and Hellinger distances are provided to compute the stochastic distance between $\mathcal {G}_{d}^{0}$ distributions. In this context, we resort to the expectation–maximization (EM) to estimate accurately with low complexity for 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.

An Incoherent Decomposition Algorithm Based on Polarimetric Symmetry for Multilook Polarimetric SAR Data

Polarimetric symmetry, including reflection symmetry, rotation symmetry, and azimuthal symmetry, are useful concepts for describing the scattering characteristics of polarimetric synthetic aperture radar (SAR) data. In this study, for the coherency matrices of multilook polarimetric SAR data, three new scattering models were first proposed based on the polarimetric symmetry. Then, a four-component incoherent decomposition algorithm was introduced, which was based on the three aforementioned new models, along with a classic volume scattering model. The proposed algorithm was considered to be a complete incoherent decomposition algorithm. No polarimetric information was lost during the decomposition. Also, there were no negative powers in its output. Then, by utilizing three polarimetric SAR images which had been derived by ESAR, RADARSAT-2, and GF-3, respectively, the experimental results had revealed that the proposed decomposition algorithm was effective in the analysis of the scattering mechanisms of terrain targets. It was observed that more than 90% of the coherency matrices of the real polarimetric SAR data had been completely decomposed by the proposed algorithm into four components. These components were found to be exactly consistent with the scattering models for surface scattering, double-bounce scattering, volume scattering, and helix scattering. The proposed decomposition algorithm had provided insight into the limitations of incoherent decomposition.

Unsupervised Change Detection in Polarimetric SAR Data With the Hotelling-Lawley Trace Statistic and Minimum-Error Thresholding

Increased discrimination capability provided by polarimetric synthetic aperture radar (PolSAR) sensors compared to single and dual polarization synthetic aperture radar (SAR) sensors can improve land use monitoring and change detection. This necessitates reliable change detection methods in multitemporal PolSAR datasets. This paper proposes an unsupervised change detection algorithm for multilook PolSAR data. In the first step of the method, the Hotelling-Lawley trace (HLT) statistic is applied to measure the similarity of two multilook covariance matrices. As a result of this step, a scalar test statistic image is generated. Then, in the second step, a generalized Kittler and Illingworth (K&I) minimum-error thresholding algorithm is developed to perform on the test statistic image and discriminate between changed and unchanged areas. The K&I thresholding algorithm is based on the generalized Gamma distribution for statistical modeling of change and no-change classes. The proposed methodology is tested on a simulated PolSAR data and two C-band fully PolSAR datasets acquired by the uninhabited aerial vehicle SAR and RADARSAT-2 SAR satellites. The experiments show that the proposed algorithm accurately discriminates between change and no-change areas providing detection results with noticeably lower error rates and higher detection accuracy values compared to those of a CFAR-type thresholding of the HLT statistic. Also, the performance of the HLT statistic compared to the other statistics applied on the multilook polarimetric SAR data is assessed. Goodness-of-fit test results prove that the estimated generalized Gamma class conditional models adequately fit the corresponding change and no-change classes.

HETEROGENEITY MEASUREMENT BASED ON DISTANCE MEASURE FOR POLARIMETRIC SAR DATA

Abstract. To effectively test the scene heterogeneity for polarimetric synthetic aperture radar (PolSAR) data, in this paper, the distance measure is introduced by utilizing the similarity between the sample and pixels. Moreover, given the influence of the distribution and modeling texture, the K distance measure is deduced according to the Wishart distance measure. Specifically, the average of the pixels in the local window replaces the class center coherency or covariance matrix. The Wishart and K distance measure are calculated between the average matrix and the pixels. Then, the ratio of the standard deviation to the mean is established for the Wishart and K distance measure, and the two features are defined and applied to reflect the complexity of the scene. The proposed heterogeneity measure is proceeded by integrating the two features using the Pauli basis. The experiments conducted on the single–look and multilook PolSAR data demonstrate the effectiveness of the proposed method for the detection of the scene heterogeneity.

Level Set Segmentation Algorithm for High-Resolution Polarimetric SAR Images Based on a Heterogeneous Clutter Model

In this paper, a heterogeneous clutter model named ${\mathcal L}$ distribution is introduced into the level set method for segmentation of high-resolution polarimetric synthetic aperture radar (PolSAR) images. Level set methods are robust and effective techniques for segmentation. However, traditional level set methods for PolSAR data are based on the complex Wishart distribution, which is not an applicable model to high-resolution PolSAR images and heterogeneous regions such as forest and urban areas. The ${\mathcal L}$ distribution is proved to be a highly flexible model for multilook PolSAR data, which is based on the product model with a generalized-gamma-distributed texture component. The ${\mathcal L}$ -model-based level set segmentation method is assessed using C-band, X-band, and L-band PolSAR data acquired by RADARSAT-2, TerraSAR-X, and ESAR sensors, respectively. Experimental results show that the ${\mathcal L}$ -distribution model has a better capacity for characterizing high-resolution PolSAR data, especially for extremely heterogeneous regions. Compared with the Wishart-model-based and Kummer-U-model-based level set methods and Markov random field based methods, it is observed that the proposed level set algorithm can obtain more precise segmentation results.

G0-WISHART DISTRIBUTION BASED CLASSIFICATION FROM POLARIMETRIC SAR IMAGES

Abstract. Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR) technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR) image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.

$\mathcal {H}$ Distribution for Multilook Polarimetric SAR Data

Polarimetric synthetic aperture radar (PolSAR) is an advanced imaging radar system, for which the acquired data provide not only the information of each channel but also the correlation between channels. To fully utilize and accurately model the multilook PolSAR data, a novel compound distribution, named the $\mathcal {H}$ distribution, is proposed based on the generalized Fisher distribution ( $\text{G}\mathcal {F}\text{D}$ ). Specifically, the $\text{G}\mathcal {F}\text{D}$ introduces a power parameter to the ordinary Fisher distribution. With one more free parameter, the $\text{G}\mathcal {F}\text{D}$ is flexible and versatile enough to characterize different kinds of texture. Then, by assuming the generalized-Fisher-distributed texture and the Wishart-distributed speckle, the $\mathcal {H}$ distribution is derived, whose closed-form expression is obtained with the help of Fox’s H-function. As such, the $\mathcal {H}$ distribution has a compact form and is conveniently applied to practical problems, such as modeling and classification of PolSAR data. The effectiveness of this method is tested by modeling the multilook PolSAR data and performing image classification. The experimental results demonstrate that the $\mathcal {H}$ distribution is a flexible and effective way to model multilook PolSAR data.

The Impacts of Building Orientation on Polarimetric Orientation Angle Estimation and Model-Based Decomposition for Multilook Polarimetric SAR Data in Urban Areas

Building orientation with respect to the radar look direction has a critical influence on the interpretation of multilook polarimetric synthetic aperture radar (PolSAR) data in urban areas. In this paper, its impacts on polarimetric orientation angle (POA) estimation and model-based decomposition are discussed. The discussion begins with the analysis of the general double-bounce scattering model, of which the characteristics are dependent on the electromagnetic and geometric parameters of the related dihedral structure. Then, for multilook PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, the impacts of the building orientation on POA estimation can be revealed. With the increase of the building orientation, the POA difference between the two dihedral structures increases gradually, and the feasibility to estimate the building orientation via the estimated POA is reduced dramatically. Upon further analysis, we illustrate the impacts on the model-based decomposition. With the increase of the building orientation, the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the processing of POA compensation, which is helpful in reducing the impacts of the building orientation, also becomes invalid when the building orientation increases to a certain value. At last, three L-band data sets of San Francisco acquired by AIRSAR are used to verify the inferences. The experimental results show that, for L-band PolSAR data in urban areas, when the radar look angle is around 45, the threshold of building orientation for the validity of dominant scattering mechanism labeling is about ±3, and for the POA compensation, the threshold is about ±12.

The Extended Polarimetric Whitening Filter and Its Application to Target Detection in Polarimetric Synthetic Aperture Radar Images

In this letter, we propose the extended polarimetric whitening filter (EPWF) for target detection in polarimetric synthetic aperture radar (PolSAR) images. We adopt finite mixture models fitted by the expectation-maximization algorithm for modeling PolSAR clutter. Specifically for single-look and multilook PolSAR data, the Gaussian mixture model (GMM) and Wishart mixture model (WMM) are applied, respectively. At the core of the EPWF, we present the closed-form solution to the optimal weighting matrix that minimizes the speckle of filtered clutter. We also derive the null hypothesis distribution of the EPWF detector to determine the detection threshold given a false alarm rate. The effectiveness of the EPWF is evaluated on both simulated and real PolSAR data. Experimental results demonstrate that the GMM and WMM are suitable for PolSAR clutter with channel-dependent texture. Compared with alternative detection statistics, the speckle level of the EPWF is lower in terms of the standard-deviation-to-mean ratio, which leads to a preferable target detection performance of the EPWF detector.

The WGΓ Distribution for Multilook Polarimetric SAR Data and Its Application

Statistical modeling for the statistics of polarimetric synthetic aperture radar (SAR) data is a critical factor in polarimetric SAR data processing. In this letter, we utilize the complex Wishart-generalized Gamma (WGΓ) distribution to model multilook polarimetric SAR data, in which the complex Wishart distribution and generalized Gamma distribution model the speckle and texture components, respectively. Moreover, we derive a closed-form expression for the WGΓ distribution based on the product model and propose a parameter estimation technique of the WGΓ distribution in this letter. We perform the experiments on the polarimetric SAR data acquired by the AIRSAR and ESAR to verify the superiority and effectiveness of the WGΓ distribution over the K and KummerU distributions in the goodness of fit of polarimetric SAR data histograms and the polarimetric SAR image classification. The experimental results demonstrate that the WGΓ distribution has a greater flexibility than the K and KummerU distributions in the statistical modeling of multilook polarimetric SAR data.

Change Detection of Multilook Polarimetric SAR Images Using Heterogeneous Clutter Models

In this paper, we present a novel unsupervised change detection scheme for multilook polarimetric synthetic aperture radar (PolSAR) images using heterogeneous clutter models. First, a multilook product model is introduced to describe the heterogeneous clutter for multilook PolSAR data, and a corresponding covariance matrix estimation method is derived. Based on this model, a new similarity measure is proposed to quantify the degree of evolution between the statistical characteristics of multitemporal fully PolSAR images. Compared with the classical similarity measure of Wishart distribution, both the structure of the covariance matrix and the power information of PolSAR clutter are considered in the proposed similarity measure. A Kittler and Illingworth (K&I) minimum-error threshold segmentation method is applied to extract the changed areas. Both the simulated PolSAR data set and two three-look Radarsat-2 fully polarimetric images of Suzhou, China, acquired on April 9, 2009 and June 15, 2010, are used for our experiment. The results demonstrate that the proposed change detection method can give a much higher detection rate and a lower false alarm rate than the method using the Wishart similarity measure.

Estimation of the Equivalent Number of Looks in Polarimetric Synthetic Aperture Radar Imagery

This paper addresses estimation of the equivalent number of looks (ENL), an important parameter in statistical modeling of multilook synthetic aperture radar (SAR) images. Two new ENL estimators are discovered by looking at certain moments of the multilook polarimetric covariance matrix, which is commonly used to represent multilook polarimetric SAR (PolSAR) data, and assuming that the covariance matrix is complex Wishart distributed. First, a second-order trace moment provides a polarimetric extension of the ENL definition and also a matrix-variate version of the conventional ENL estimator. The second estimator is obtained from the log-determinant matrix moment and is also shown to be the maximum likelihood estimator under the Wishart model. It proves to have much lower variance than any other known ENL estimator, whether applied to single-polarization or PolSAR data. Moreover, this estimator is less affected by texture and thus provides more accurate results than other estimators should the assumption of Gaussian statistics for the complex scattering coefficients be violated. These are the first known estimators to use the full covariance matrix as input, rather than individual intensity channels, and therefore to utilize all the statistical information available. We finally demonstrate how an ENL estimate can be computed automatically from the empirical density of small sample estimates calculated over a whole scene. We show that this method is more robust than procedures where the estimate is calculated in a manually selected region of interest.

Fisher Distribution for Texture Modeling of Polarimetric SAR Data

The multilook polarimetric synthetic aperture radar (PolSAR) covariance matrix is generally modeled by a complex Wishart distribution. For textured areas, the product model is used, and the texture component is modeled by a Gamma distribution. In many cases, the assumption of Gamma-distributed texture is not appropriate. The Fisher distribution does not have this limitation and can represent a large set of texture distributions. As an example, we examine its advantage for an urban area. From a Fisher-distributed texture component, we derive the distribution of the complex covariance matrix for multilook PolSAR data. The obtained distribution is expressed in terms of the KummerU confluent hypergeometric function of the second kind. Those distributions are related to the Mellin transform and second-kind statistics (Log-statistics). The new KummerU-based distribution should provide in many cases a better representation of textured areas than the classic <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> distribution. Finally, we show that the new model can discriminate regions with different texture distribution in a segmentation experiment with synthetic textured PolSAR images.

Classification of multi-look polarimetric SAR imagery and polarization channel optimization

In this paper, a new maximum likelihood (ML) classification algorithm is proposed to classify the multi-look polarimetric synthetic aperture radar (SAR) imagery. Experimental results with the NASA/JPL airborne L-band polarimetric SAR data demonstrate the effectiveness of the new algorithm. Furthermore, when using the algorithm in the classifications with subsets of the multi-look polarimetric SAR data, the polarization-channel optimization for the terrain type classification is implemented.

Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution

Abstract Multi-look polarimetric SAR (synthetic aperture radar) data can be represented either in Mueller matrix form or in complex covariance matrix form. The latter has a complex Wishart distribution. A maximum likelihood classifier to segment polarimetric SAR data according to terrain types has been developed based on the Wishart distribution. This algorithm can also be applied to multifrequency multi-look polarimetric SAR data, as well as 10 SAR data containing only intensity information. A procedure is then developed for unsupervised classification. The classification error is assessed by using Monte Carlo simulation of multilook polarimetric SAR data, owing to the lack of ground truth for each pixel. Comparisons of classification errors using the training sets and single-look data are also made. Applications of this algorithm are demonstrated with NASA/JPL P-, L- and C-band polarimetric SAR data.