Supervised classication allows handling a wide range of remote sensing hyperspectral applications. Enhancing
the spatial organization of the pixels over the image has proven to be benecial for the interpretation of the
image content, thus increasing the classication accuracy. Denoising in the spatial domain of the image has
been shown as a technique that enhances the structures in the image. This paper proposes a multi-component
denoising approach in order to increase the classication accuracy when a classication method is applied. It
is computed on multicore CPUs and NVIDIA GPUs. The method combines feature extraction based on a 1Ddiscrete
wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological
Prole (EMP) and a classier (SVM or ELM). The multi-component noise reduction is applied to the EMP
just before the classication. The denoising recursively applies a separable 2D DWT after which the number of
wavelet coecients is reduced by using a threshold. Finally, inverse 2D-DWT lters are applied to reconstruct
the noise free original component. The computational cost of the classiers as well as the cost of the whole
classication chain is high but it is reduced achieving real-time behavior for some applications through their
computation on NVIDIA multi-GPU platforms.
Keywords: Land cover classication, Hyperspectral analysis, Wavelet transform, Denoising, Spectral-spatial processing, High-Performance computing, Multi-thread, Multi-GPU.