SAR Image Texture Classification Based on Kernel Fisher Discriminant Analysis
Binbin He, Ling Tong, Xiaolin Han, Wenbo Xu · 2006
Texture analysis plays an important role in remote sensing image processing. Most methods of texture classification, for the identification of different texture surfaces, are based on wavelet features, MRF models, STFT features, and geometric shape of texels and PCA analysis. Kernel Fisher discriminant (KFD) is a state-of-the-art nonlinear machine learning method, and it has great potential to identify image texture. Meanwhile, Texture analysis is also an important technique of SAR image classification. In this paper, a nonlinear discriminative texture feature extraction method based on kernel Fisher discriminant (KFD) is proposed for SAR image texture classification.