RT journal article
T1 GPU Projection of ECAS-II Segmenter for Hyperspectral Images Based on Cellular Automata
A1 López Fandiño, Javier
A1 Priego Torres, Blanca María
A1 Blanco Heras, Dora
A1 Argüello, Francisco
A1 Duro, Richard J.
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 Cellular automata (CA)
K1 CUDA
K1 evolutionary cellular automata segmentation (ECAS-II)
K1 Extreme learning machines (ELM)
K1 Graphics processor unit (GPU)
K1 Hyperspectral images
K1 Segmentation
AB Segmentation plays a crucial role in the analysis of multidimensional images, such as those used in remote sensing. Typically, segmentation algorithms for these images start by reducing their dimensionality, which can lead to the loss of potentially important information for the segmentation process. Evolutionary cellular automata segmentation (ECAS-II) offers an alternative by utilizing a cellular automata-based approach that considers all the spectral information in a hyperspectral image, without resorting to dimensionality reduction techniques. This paper introduces an efficient implementation of ECAS-II on a graphics processing unit (GPU) for segmenting hyperspectral land cover images. The proposed method is integrated into a spectral-spatial classification framework based on extreme learning machines (ELM). Experimental results indicate that this approach provides better accuracy for land cover classification compared to other segmentation-based spectral-spatial classification techniques.
SN 1939-1404
YR 2017
FD 2017
LK http://hdl.handle.net/10498/34749
UL http://hdl.handle.net/10498/34749
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026