Wavelet-based multicomponent denoising on GPU to improve the classification of hyperspectral images

Supervised classication allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be benecial for the interpretation of the image content, thus increasing the classication 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 classication accuracy when a classication 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 Prole (EMP) and a classier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classication. 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 classiers as well as the cost of the whole classication chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.