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Spectral-spatial pre-processing using multi-resolution 3D wavelets for hyperspectral image classification

Abstract : In the context of precision agriculture, hyperspectral image classification has already proved its potential to classify highly similar vegetation that could not be achieved with standard RGB camera. However, the in-field conditions do not allow a perfect classification while using conventional methods that only deal with spectral information. Several study already showed the potential of methods taking into account the spatial information embedded within hyperspectral images. In this paper, the spectral-spatial information is enhanced using a 3D multi-resolution discrete wavelet decomposition of the hyperspectral image. The smoothed image is obtained by zeroing noisy coefficients in the appropriate decomposed data cube before wavelet reconstruction. Then, Partial-Least-Squares Linear-Discriminant-Analysis is applied for classification. Experimental results obtained with this method outperformed standard spectral pre-processing and also non-linear classification method such as SVM.
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Xavier Hadoux, Nathalie Gorretta, Gilles Rabatel, Jean-Michel Roger, Olivier Strauss. Spectral-spatial pre-processing using multi-resolution 3D wavelets for hyperspectral image classification. 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Jun 2013, Gainesville, FL, United States. pp.1-4, ⟨10.1109/WHISPERS.2013.8080709⟩. ⟨hal-02599309⟩



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