Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges

被引：49
|
作者：
Deng, Xinping [1 ]
Lopez-Martinez, Carlos [2 ]
Chen, Jinsong [1 ]
Han, Pengpeng [1 ]
机构：
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
[2] Univ Politecn Cataluna, Signal Theory & Commun Dept, Remote Sensing Lab, ES-08034 Barcelona, Spain
来源：
REMOTE SENSING | 2017年 / 9卷 / 04期
关键词：
statistical modeling; polarimetric SAR; texture models; finite mixture models; copulas; TARGET DECOMPOSITION-THEOREMS; UNSUPERVISED CLASSIFICATION; FUNDAMENTAL PROPERTIES; MULTITEXTURE MODEL; PHASE STATISTICS; TEXTURE; IMAGES; CLUTTER; MATRIX; SEGMENTATION;
D O I：
10.3390/rs9040348
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Knowledge of the exact statistical properties of the signal plays an important role in the applications of Polarimetric Synthetic Aperture Radar (PolSAR) data. In the last three decades, a considerable research effort has been devoted to finding accurate statistical models for PolSAR data, and a number of distributions have been proposed. In order to see the differences of various models and to make a comparison among them, a survey is provided in this paper. Texture models, which could capture the non-Gaussian behavior observed in high resolution data, and yet keep a compact mathematical form, are mainly explained. Probability density functions for the single look data and the multilook data are reviewed, as well as the advantages and applicable context of those models. As a summary, challenges in the area of statistical analysis of PolSAR data are also discussed.
引用
收藏
页数：34
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