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Proceedings Paper

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images
Author(s): Pablo Quesada-Barriuso; Dora B. Heras; Francisco Argüello
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Paper Abstract

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

Paper Details

Date Published: 18 October 2016
PDF: 16 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040R (18 October 2016); doi: 10.1117/12.2240854
Show Author Affiliations
Pablo Quesada-Barriuso, Ctr. Singular de Investigación en Tecnoloxías da Información (Spain)
Dora B. Heras, Ctr. Singular de Investigación en Tecnoloxías da Información (Spain)
Francisco Argüello, Univ. de Santiago de Compostela (Spain)


Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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