RT Journal Article
T1 GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images
A1 Quesada Barriuso, Pablo
A1 Blanco Heras, Dora
A1 Argüello Pedreira, Francisco
K1 Superpixel segmentation
K1 Watershed transform
K1 Waterpixel segmentation
K1 Hyperspectral image
K1 Remote sensing
K1 CUDA
AB The high computational cost of the superpixel segmentation algorithms for hyperspectral remote sensing images makes them ideal candidates for parallel computation. The waterpixel algorithm, in particular, extracts segmentation regions called waterpixels and consists of four stages called vectorial gradient, spatial regularization, marker selection, and watershed transform. In this paper, an efficient version of a GPU algorithm for waterpixel segmentation using the Compute Unified Device Architecture (CUDA) is presented. The algorithm extracts all the spectral information available in the bands of the hyperspectral image through the vectorial gradient. A cellular automaton is selected for the computation of the watershed transform using a block-asynchronous implementation with 8-connectivity. The experimental analysis shows high speedup values for the resulting GPU algorithm when it is compared to a multicore OpenMP implementation using 8 threads.
PB Springer
SN 1573-0484
YR 2021
FD 2021-02-22
LK https://hdl.handle.net/10347/38537
UL https://hdl.handle.net/10347/38537
LA spa
NO Quesada-Barriuso, P., Blanco Heras, D., & Argüello, F. (2021). GPU accelerated waterpixel algorithm for superpixel segmentation of hyperspectral images. The Journal of Supercomputing, 77(9), 10040-10052.
NO This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11227-021-03666-y
NO Ministerio de Ciencia e Innovación, Gobierno de España, PID2019-104834GB-I00
NO Consellerı́a de Educación, Universidade e Formación Profesional, Xunta de Galicia, ED431C 2018/19 y 2019-2022 ED431G-2019/04
NO Junta de Castilla y León, VA226P20
NO European Regional Development Fund, ERDF
DS Minerva
RD 27 abr 2026