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Improvement of Classification Based on Noise and Spectral Dimensionality Reduction for Hyperspectral Image

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DOI: 10.23977/geors.2018.11012 | Downloads: 29 | Views: 3684


Salah Bourennane 1, Caroline Fossati 1, Josselin Juan 1


1 Aix Marseille Univ, CNRS, Centrale Marseille, Institut Fresnel, Marseille, France

Corresponding Author

Salah Bourennane


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.
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.


Classification, Tensor, Reduction, Hyperspectral image.


Salah Bourennane, Caroline Fossati and Josselin Juan, Improvement of Classification Based on Noise and Spectral Dimensionality Reduction for Hyperspectral Image. Geoscience and Remote Sensing (2018) 1: 9-17.


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