Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges
AbstractKnowledge 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
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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.
Deng X, López-Martínez C, Chen J, Han P. Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges. Remote Sensing. 2017; 9(4):348.Chicago/Turabian Style
Deng, Xinping; López-Martínez, Carlos; Chen, Jinsong; Han, Pengpeng. 2017. "Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges." Remote Sens. 9, no. 4: 348.
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