Texture provides valuable information for synthetic aperture radar (SAR) image classification, especially when the single-band and single-polarized SAR is concerned. Three texture feature extraction methods including the gray-level co-occurrence matrix; the gray-gradient co-occurrence matrix and the energy measures of the undecimated wavelet decomposition are introduced to represent the textural information of SAR image. However, the simple combination of these features with each other is usually not suitable for SAR image classification due to the resulting redundancy and the additive computation complexity. Based on immune clonal selection algorithm, a new feature selection approach characterized by rapid convergence to global optimal solution is proposed and applied to find the optimal feature subset. Based on the features selected, SVMs are used to classify the land covers in SAR images. The effectiveness of feature subset selected and the validity of the proposed method are well verified by the experiment results.

Unable to display preview. Download preview PDF.

Authors and Affiliations

1. National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, 710071, Xi’an, China

   Xiangrong Zhang, Tan Shan & Licheng Jiao

Editors and Affiliations

1. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

   Aurélio Campilho

2. Electrical and Computer Engineering Department, University of Waterloo,  

   Mohamed Kamel

Reprints and permissions

© 2004 Springer-Verlag Berlin Heidelberg

Policies and ethics