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Remote Sens. 2017, 9(4), 348;

Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Remote Sensing Laboratory, Signal Theory and Communications Department, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Author to whom correspondence should be addressed.
Received: 6 March 2017 / Revised: 29 March 2017 / Accepted: 2 April 2017 / Published: 5 April 2017
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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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. View Full-Text
Keywords: statistical modeling; polarimetric SAR; texture models; finite mixture models; copulas statistical modeling; polarimetric SAR; texture models; finite mixture models; copulas

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Deng, X.; López-Martínez, C.; Chen, J.; Han, P. Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges. Remote Sens. 2017, 9, 348.

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