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Stochastic contrast Measures for SAR Data: A Survey

雷达学报（中英文） 2019年第6期8卷 758-781页

作者： alejandro c.frery La ccAN–Laboratório de computa??o científica e Análise Numérica Universidade Federal de Alagoas

“contrast”is an generic denomination for“difference”.Measures of contrast are a powerful tool in image processing and analysis,e.g.,in denoising,edge detection,segmentation,classification,parameter estimation,change detection,and feature *** present a survey on techniques that aim at measuring the contrast between(i)samples of SAR imagery,and(ii)samples and models,with emphasis on those that employ the statistical properties of the data.

关键词： contrast Divergence Entropy Geodesic distance Statistics Synthetic Aperture Radar(SAR)

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

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International Journal of Digital Earth 2019年第6期12卷 699-719页

作者： Rogerio G.Negri alejandro c.frery Wagner B.Silva Tatiana S.G.Mendes Luciano V.Dutra UNESP-Universidade Estadual Paulista ICT-Instituto de Ciencia e TecnologiaSão Jose dos CamposBrazil LaccAN-Laboratorio de de computacao cientifica e Analise Numerica UFAL-Universidade Federal de AlagoasMaceioBrazil Secao de Ensino de Engenharia cartografica IME-Instituto Militar de EngenhariaRio de JaneiroBrazil DPI-Divisao de Processamento de Imagens INPE-Instituto Nacional de Pesquisas EspaciaisSao Jose dos CamposBrazil

Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and *** et al.[“classification of segments in PolSAR imagery by minimum stochastic distances between wishart distributions.”IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(3):1263–1273]used stochastic distances between complex multivariate Wishart models which,differently from other measures,are computationally *** this work we assess the robustness of such approach with respect to errors in the training stage,and propose an extension that alleviates such *** 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 *** 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 *** this,we model the situation of imperfect training *** 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 *** and data are provided for reproducibility.

关键词： PolSAR image classification stochastic distance minimum distance classifier SVM

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