Abstract
Hyperspectral image (HSI) classification requires spectral dimensionalityreduction and spatial filtering. While common dimensionality reduction and denoising methods use linear algebra, we propose a tensorial method to jointly achieve denoising and dimensionality reduction.<br /> Firstly, we propose a new method for pre-whitening the noise (PW) in HSI. Then we propose a method based on quadtree decomposition adapted to tensor data in order to take into account the local image characteristics in the multi-way Wiener filter (LMWF) which performs both noise and spectral dimensionality reduction, referred to as PW-LMWFdr-(K1;K2;P3). Classification algorithm SVM is applied to the output of dimensionality and noise reduction methods to compare their efficiency: The proposed PW-LMWFdr-(K1;K2;P3), PW-MWFdr-(K1;K2;P3), PCAdr,MNFdr associated with Wiener filtering.
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