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- W2151022383 abstract "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." @default.
- W2151022383 created "2016-06-24" @default.
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- W2151022383 date "2006-07-01" @default.
- W2151022383 modified "2023-09-23" @default.
- W2151022383 title "SAR Image Texture Classification Based on Kernel Fisher Discriminant Analysis" @default.
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- W2151022383 doi "https://doi.org/10.1109/igarss.2006.802" @default.
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