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Open AccessArticle

Multi-GPU Based Parallel Design of the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images

1
Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), Beijing 100094, China
2
China Academy of Space Technology (CAST), Beijing 100081, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
College of Computer Science and Software Engineering, Computer Vision Research Institute, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 598; https://doi.org/10.3390/s19030598
Received: 30 November 2018 / Revised: 25 January 2019 / Accepted: 26 January 2019 / Published: 31 January 2019
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
Spectral unmixing is a vital procedure in hyperspectral remote sensing image exploitation. The linear mixture model has been widely utilized to unmix hyperspectral images by extracting a set of pure spectral signatures, called endmembers in hyperspectral jargon, and estimating their respective fractional abundances in each pixel of the scene. Many algorithms have been proposed to extract endmembers automatically, which is a critical step in the spectral unmixing chain. In recent years, the ant colony optimization (ACO) algorithm has been developed for endmember extraction from hyperspectral data, which was regarded as a combinatorial optimization problem. Although the ACO for endmember extraction (ACOEE) can acquire accurate endmember results, its high computational complexity has limited its application in the hyperspectral data analysis. The GPUs parallel computing technique can be utilized to improve the computational performance of ACOEE, but the architecture of GPUs determines that the ACOEE should be redesigned to take full advantage of computing resources on GPUs. In this paper, a multiple sub-ant-colony-based parallel design of ACOEE was proposed, in which an innovative mechanism of local pheromone for sub-ant-colonies is utilized to enable ACOEE to be preferably executed on the multi-GPU system. The proposed method can avoid much synchronization among different GPUs to affect the computational performance improvement. The experiments on two real hyperspectral datasets demonstrated that the computational performance of ACOEE significantly benefited from the proposed methods. View Full-Text
Keywords: hyperspectral images; endmember extraction; multi-GPU; ant colony optimization (ACO); parallel computing hyperspectral images; endmember extraction; multi-GPU; ant colony optimization (ACO); parallel computing
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MDPI and ACS Style

Gao, J.; Sun, Y.; Zhang, B.; Chen, Z.; Gao, L.; Zhang, W. Multi-GPU Based Parallel Design of the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images. Sensors 2019, 19, 598.

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