RT Journal Article
T1 Wavelet-Based Multicomponent Denoising Profile for the Classification of Hyperspectral Images
A1 González Bascoy, Pedro
A1 Quesada Barriuso, Pablo
A1 Blanco Heras, Dora
A1 Argüello Pedreira, Francisco
K1 Classification
K1 Denoising
K1 Profile
K1 Remote sensing
K1 Wavelet transform
AB The high resolution of the hyperspectral remote sensing images available allows the detailed analysis of even small spatial structures. As a consequence, the study of techniques to efficiently extract spatial information is a very active realm. In this paper, we propose a novel denoising wavelet-based profile for the extraction of spatial information that does not require parameters fixed by the user. Over each band obtained by a wavelet-based feature extraction technique, a denoising profile (DP) is built through the recursive application of discrete wavelet transforms followed by a thresholding process. Each component of the DP consists of features reconstructed by recursively applying inverse wavelet transforms to the thresholded coefficients. Several thresholding methods are explored. In order to show the effectiveness of the extended DP (EDP), we propose a classification scheme based on the computation of the EDP and supervised classification by extreme learning machine. The obtained results are compared to other state-of-the-art methods based on profiles in the literature. An additional study of behavior in the presence of added noise is also performed showing the high reliability of the EDP proposed
PB IEEE
SN 1939-1404
YR 2019
FD 2019
LK http://hdl.handle.net/10347/24654
UL http://hdl.handle.net/10347/24654
LA eng
NO Pedro G. Bascoy, Pablo Quesada-Barriuso, Dora B. Heras and Fancisco Argüello (2019) Wavelet-Based Multicomponent Denoising Profile for the Classification of Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (2), 722 - 733. Doi: 10.1109/JSTARS.2019.2892990
NO This work was supported in part by the Consellería de Educación, Universidade e Formación Profesional under Grants GRC2014/008 and ED431C 2018/2019 and the Ministerio de Economía y Empresa, Gobierno de España under Grant TIN2016-76373-P. Both are cofunded by the European Regional Development Fund
DS Minerva
RD 24 abr 2026