Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges

Deng, Xiangquan; López-Martínez, Carlos; Chen, Jinsong; Han, Pengpeng

doi:10.3390/rs9040348

Cited by 48 publications

(19 citation statements)

References 64 publications

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“…In the BSA coordinate system, the reciprocity theorem says that the cross-pol channels of the scattering matrix are equal, i.e., S vh = S vh . Usually, for the studying of PolSAR statistical behavior, is more convenient to represent the matrix S as a lexicographic basis vector [18]. Therefore, under the reciprocity theorem, the scattering vector could be expressed as:…”

Section: Polsar Image Representationmentioning

confidence: 99%

Analysis of Stochastic Distances and Wishart Mixture Models Applied on PolSAR Images

2019

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This paper address unsupervised classification strategies applied to Polarimetric Synthetic Aperture Radar (PolSAR) images. We analyze the performance of complex Wishart distribution, which is a widely used model for multi-look PolSAR images, and the robustness of five stochastic distances (Bhattacharyya, Kullback-Leibler, Rényi, Hellinger and Chi-square) between Wishart distributions. Two unsupervised classification strategies were chosen: the Stochastic Clustering (SC) algorithm, which is based on the K-means algorithm but uses stochastic distance as the similarity metric, and the Expectation-Maximization (EM) algorithm for Wishart Mixture Model. With the aim of assessing the performance of all algorithms presented here, we performed a Monte Carlo simulation over a set of simulated PolSAR images. A second experiment was conducted using the study area of Tapajós National Forest and the surrounding area, in Brazilian Amazon Forest. The PolSAR images were obtained by the satellite PALSAR. The results, in both experiments, suggest that the EM algorithm and the SC with Hellinger and the SC with Bhattacharyya distance provide a better classification performance. We also analyze the initialization problem for SC and EM algorithms, and we demonstrate how the initial centroid choice influences the final classification result.

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Section: Polsar Image Representationmentioning

confidence: 99%

Analysis of Stochastic Distances and Wishart Mixture Models Applied on PolSAR Images

2019

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“…The scaled Complex Multivariate Wishart model is valid in textureless areas. Target variability can be included by one or more additional parameters; the reader is referred to the work by Deng et al (2017) for a comprehensive survey of models for PolSAR data.…”

Section: Statistical Polsar Modellingmentioning

confidence: 99%

Region-based classification of PolSAR data using radial basis kernel functions with stochastic distances

Negri

Frery

Silva

et al. 2018

International Journal of Digital Earth

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Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al. (2013) used stochastic distances between complex multivariate Wishart models which, differently from other measures, are computationally tractable. In this work we assess the robustness of such approach with respect to errors in the training stage, and propose an extension that alleviates such problems. We introduce robustness in the process by incorporating a combination of radial basis kernel functions and stochastic distances with Support Vector Machines (SVM). We consider several stochastic distances between Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square, Rényi, and Hellinger. We perform two case studies with PolSAR images, both simulated and from actual sensors, and different classification scenarios to compare the performance of Minimum Distance and SVM classification frameworks. With this, we model the situation of imperfect training samples. We show that SVM with the proposed kernel functions achieves better performance with respect to Minimum Distance, at the expense of more computational resources and the need of parameter tuning. Code and data are provided for reproducibility.

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“…Regarding the estimation of p(I(x)|H 0 ) and p(I(x)|H 1 ), a lot of studies have worked on the backscattering statistical models for SAR and PolSAR images [31,32]. Among them, the multiplicative model is popular and widely used, which is based on the assumption that the observed random value Z is the product of two independent and unobserved random fields: X and Y.…”

Section: Local and Global Single-scale Saliency Measurementioning

confidence: 99%

Saliency Map Generation for SAR Images with Bayes Theory and Heterogeneous Clutter Model

Xiang

Tang

et al. 2017

Remote Sensing

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Saliency map generation in synthetic aperture radar (SAR) imagery has become a promising research area, since it has a close relationship with quick potential target identification, rescue services, etc. Due to the multiplicative speckle noise and complex backscattering in SAR imagery, producing satisfying results is still challenging. This paper proposes a new saliency map generation approach for SAR imagery using Bayes theory and a heterogeneous clutter model, i.e., the G 0 model. With Bayes theory, the ratio of the probability density functions (PDFs) in the target and background areas contributes to the saliency. Local and global background areas lead to different saliency measures, i.e., local saliency and global saliency, which are combined to make a final saliency measure. To measure the saliency of targets of different sizes, multiscale saliency enhancement is conducted with different region sizes of target and background areas. After collecting all of the salient regions in the image, the result is refined by considering the image's immediate context. The saliency of regions that are far away from the focus of attention is suppressed. Experimental results with two single-polarization and two multi-polarization SAR images demonstrate that the proposed method has better speckle noise robustness, higher accuracy, and more stability in saliency map generation both with and without the complex background than state-of-the-art methods. The saliency map accuracy can achieve above 95% with four datasets, which is about 5-20% higher than other methods.

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Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges

Cited by 48 publications

References 64 publications

Analysis of Stochastic Distances and Wishart Mixture Models Applied on PolSAR Images

Analysis of Stochastic Distances and Wishart Mixture Models Applied on PolSAR Images

Region-based classification of PolSAR data using radial basis kernel functions with stochastic distances

Saliency Map Generation for SAR Images with Bayes Theory and Heterogeneous Clutter Model

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