A TEST STATISTIC FOR HIGH RESOLUTION POLARIMETRIC SAR DATA CLASSIFICATION

被引：1

作者：

Formont, Pierre ^{[1
,2
]}

Ovarlez, Jean-Philippe ^{[1
,2
]}

Pascal, Frederic ^{[2
]}

Vasile, Gabriel ^{[3
]}

Ferro-Famil, Laurent ^{[4
]}

机构：

[1] ONERA DEMR TSI, French Aerosp Lab, Chemin Huniere, F-91761 Palaiseau, France

[2] SONDRA Res Alliance, F-91192 Gif Sur Yvette, France

[3] CNRS, Grenoble Image Speech Signal Automatics Lab, GIPSA Lab DIS SIGMAPHY, Grenoble INP, F-38402 St Martin Dheres, France

[4] CNRS, Inst Elect Telecommun Rennes, IETR, F-35042 Rennes, France

来源：

2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2010年

关键词：

Image classification; polarimetry; synthetic aperture radar; statistics; NOISE;

D O I：

10.1109/IGARSS.2010.5651074

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Modern SAR systems have high resolution which leads the backscattering clutter to be non-Gaussian. In order to properly classify images from these systems, a non-Gaussian noise model is considered: the SIRV model. A statistical test of equality of covariance matrices is used to classify pixels, taking into account the critical region of the test which rejects the likeliness of a covariance matrix to any of the class centers. This test is applied on experimental data obtained with the ONERA RAMSES system in X-band. The results show a good separation between natural and man-made areas of the image.

引用

页码：1871 / 1874

页数：4

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