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Remote Sens. 2019, 11(2), 152;

An Efficient Framework for Remote Sensing Parallel Processing: Integrating the Artificial Bee Colony Algorithm and Multiagent Technology

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Department of Geography, University of South Carolina, Columbia, SC 29208, USA
Author to whom correspondence should be addressed.
Received: 5 December 2018 / Revised: 9 January 2019 / Accepted: 11 January 2019 / Published: 15 January 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Remote sensing (RS) image processing can be converted to an optimization problem, which can then be solved by swarm intelligence algorithms, such as the artificial bee colony (ABC) algorithm, to improve the accuracy of the results. However, such optimization algorithms often result in a heavy computational burden. To realize the intrinsic parallel computing ability of ABC to address the computational challenges of RS optimization, an improved multiagent (MA)-based ABC framework with a reduced communication cost among agents is proposed by utilizing MA technology. Two types of agents, massive bee agents and one administration agent, located in multiple computing nodes are designed. Based on the communication and cooperation among agents, RS optimization computing is realized in a distributed and concurrent manner. Using hyperspectral RS clustering and endmember extraction as case studies, experimental results indicate that the proposed MA-based ABC approach can effectively improve the computing efficiency while maintaining optimization accuracy. View Full-Text
Keywords: remote sensing; optimization; parallel processing; multiagent remote sensing; optimization; parallel processing; multiagent

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Yang, L.; Sun, X.; Li, Z. An Efficient Framework for Remote Sensing Parallel Processing: Integrating the Artificial Bee Colony Algorithm and Multiagent Technology. Remote Sens. 2019, 11, 152.

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