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Proceedings Paper

Wavelet-based multicomponent denoising on GPU to improve the classification of hyperspectral images
Author(s): Pablo Quesada-Barriuso; Dora B. Heras; Francisco Argüello; J. C. Mouriño
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Paper Abstract

Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification 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 classification accuracy when a classification 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 Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.

Paper Details

Date Published: 5 October 2017
PDF: 16 pages
Proc. SPIE 10430, High-Performance Computing in Geoscience and Remote Sensing VII, 104300C (5 October 2017); doi: 10.1117/12.2277960
Show Author Affiliations
Pablo Quesada-Barriuso, Ctr. Singular de Investigación en Tecnoloxías da Información (Spain)
Dora B. Heras, Univ. de Santiago de Compostela (Spain)
Francisco Argüello, Univ. de Santiago de Compostela (Spain)
J. C. Mouriño, Lab. Oficial de Metroloxia de Galicia (Spain)

Published in SPIE Proceedings Vol. 10430:
High-Performance Computing in Geoscience and Remote Sensing VII
Bormin Huang; Sebastián López; Zhensen Wu, Editor(s)

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