A Three-Component Fisher-Based Feature Weighting Method for Supervised PolSAR Image Classification

被引：31

作者：

Chen, Bo ^{[1
]}

Wang, Shuang ^{[1
]}

Jiao, Licheng ^{[1
]}

Stolkin, Rustam ^{[2
]}

Liu, Hongying ^{[1
]}

机构：

[1] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Int Res Ctr Intelligent Percept & Computat, Xian 710071, Peoples R China

[2] Univ Birmingham, Sch Mech Engn, Birmingham B15 2TT, W Midlands, England

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2015年 / 12卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Feature weighting; Fisher linear discriminant; polarimetric synthetic aperture radar (PolSAR); radar polarimetry; supervised image classification; three-component model-based decomposition; SCATTERING POWER DECOMPOSITION; MODEL; FILTER;

D O I：

10.1109/LGRS.2014.2360421

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

This letter presents a feature weighting method for polarimetric synthetic aperture radar (PolSAR) image classification. Appropriate feature weighting is essential for obtaining accurate classifications but so far has remained an open research problem. We propose in this letter a supervised three-component feature weighting method based on the Fisher linear discriminant. Fisher linear discriminant method is used to calculate a coefficient for each feature. Then, these coefficients are modified according to a three-component scattering power decomposition model, combining both physical and statistical scattering characteristics to adapt them for the particular scattering mechanisms inherent in PolSAR data and assigned to the coherency matrix to enhance the discriminating ability of the features. Freeman decomposition and Wishart classifier are used to classify the PolSAR image. The effectiveness of the proposed method is demonstrated by experiments NASA/JPL AIRSAR L-band and CSA Radarsat-2 C-band PolSAR images of the San Francisco area.

引用

页码：731 / 735

页数：5

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