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GPU implementation of discrete particle swarm optimization algorithm for endmember extraction from hyperspectral image
Author(s): Chaoyin Yu; Zhengwu Yuan; Yuanfeng Wu
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Paper Abstract

Hyperspectral image unmixing is an important part of hyperspectral data analysis. The mixed pixel decomposition consists of two steps, endmember (the unique signatures of pure ground components) extraction and abundance (the proportion of each endmember in each pixel) estimation. Recently, a Discrete Particle Swarm Optimization algorithm (DPSO) was proposed for accurately extract endmembers with high optimal performance. However, the DPSO algorithm shows very high computational complexity, which makes the endmember extraction procedure very time consuming for hyperspectral image unmixing. Thus, in this paper, the DPSO endmember extraction algorithm was parallelized, implemented on the CUDA (GPU K20) platform, and evaluated by real hyperspectral remote sensing data. The experimental results show that with increasing the number of particles the parallelized version obtained much higher computing efficiency while maintain the same endmember exaction accuracy.

Paper Details

Date Published: 5 October 2017
PDF: 10 pages
Proc. SPIE 10430, High-Performance Computing in Geoscience and Remote Sensing VII, 104300G (5 October 2017); doi: 10.1117/12.2279983
Show Author Affiliations
Chaoyin Yu, Chongqing Univ. of Posts and Telecommunications (China)
Institute of Remote Sensing and Digital Earth (China)
Zhengwu Yuan, Chongqing Univ. of Posts and Telecommunications (China)
Yuanfeng Wu, Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 10430:
High-Performance Computing in Geoscience and Remote Sensing VII
Bormin Huang; Sebastián López; Zhensen Wu, Editor(s)

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