RT Journal Article
T1 Extended Anisotropic Diffusion Profiles in GPU for Hyperspectral Imagery
A1 Acción Montes, Álvaro
A1 Argüello Pedreira, Francisco
A1 Blanco Heras, Dora
K1 Anisotropic diffusion profile
K1 CUDA
K1 Hyperspectral
K1 Nonlinear diffusion
K1 Remote sensing
AB Morphological profiles are a common approach for extracting spatial information from remote sensing hyperspectral images by extracting structural features. Other profiles can be built based on different approaches such as, for example, differential morphological profiles, or attribute profiles. Another technique used for characterizing spatial information on the images at different scales is based on computing profiles relying on edge-preserving filters such as anisotropic diffusion filters. Their main advantage is the preservation of the distinctive morphological features of the images at the cost of an iterative calculation. In this article, the high computational cost associated with the construction of anisotropic diffusion profiles (ADPs) is highly reduced. In particular, we propose a low-cost computational approach for computing ADPs on Nvidia GPUs as well as a detailed characterization of the method, comparing it in terms of accuracy and structural similarity to other existing alternatives
PB IEEE
SN 1939-1404
YR 2019
FD 2019
LK http://hdl.handle.net/10347/24651
UL http://hdl.handle.net/10347/24651
LA eng
NO Álvaro Acción, Francisco Argüello and Dora B. Heras (2019) Extended Anisotropic Diffusion Profiles in GPU for Hyperspectral Imagery.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (12), 4964-4976. Doi: 10.1109/JSTARS.2019.2939857
NO This work was supported in part by the Consellería de Educación, Universidade e Formación Profesional under Grants GRC2014/008, ED431C 2018/19, and ED431G/08, in part by Ministerio de Economía y Empresa, Government of Spain under Grant TIN2016-76373-P, and in part by the European Regional Development Fund
DS Minerva
RD 24 abr 2026