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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 2683248, 17 pages
Research Article

Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization

1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou 310018, China

Correspondence should be addressed to Xiaorun Li; nc.ude.ujz@ylrxl

Received 16 March 2017; Revised 31 May 2017; Accepted 3 July 2017; Published 6 August 2017

Academic Editor: Qunming Wang

Copyright © 2017 Shuhan Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Subpixel mapping technology can determine the specific location of different objects in the mixed pixel and effectively solve the uncertainty of the ground features spatial distribution in traditional classification technology. Existing methods based on linear optimization encounter the premature and local convergence of the optimization algorithm. This paper proposes a subpixel mapping method based on modified binary quantum particle swarm optimization (MBQPSO) to solve the above issues. The initial subpixel mapping imagery is obtained according to spectral unmixing results. We focus mainly on the discretization of QPSO, which is implemented by modifying the discrete update process of particle location, to minimize the objective function, which is formulated based on different connected regional perimeter calculating methods. To reduce time complexity, a target optimization strategy of global iteration combined with local iteration is performed. The MBQPSO is tested on standard test functions and results show that MBQPSO has the best performance on global optimization and convergent rate. Then, we analyze the proposed algorithm qualitatively and quantitatively by simulated and real experiment; results show that the method combined with MBQPSO and objective function, which is formulated based on the gap length between region and background, has the best performance in accuracy and efficiency.