[1]LONG Haixia,WU Shulei,LYU Yan.Classification of multispectral remote sensing image based on QPSO and diversity-mutation[J].CAAI Transactions on Intelligent Systems,2015,10(6):938-942.[doi:10.11992/tis.201507045]
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Classification of multispectral remote sensing image based on QPSO and diversity-mutation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 6 Page number: 938-942 Column: 学术论文—机器学习 Public date: 2015-12-25
Title:
Classification of multispectral remote sensing image based on QPSO and diversity-mutation
Author(s):
LONG Haixia;  WU Shulei;  LYU Yan
School of Information Science and Technology, Hainan Normal University, Haikou 571158, China
Keywords:
remote sensing image; un-supervised classification; cluster centers; quantum-behaved particle swarm optimization algorithm; diversity-mutation
CLC:
TP391.9
DOI:
10.11992/tis.201507045
Abstract:
The classification of remote sensing images is one of the most important issues in remote sensing today. This paper presents a novel classification algorithm for multispectral remote sensing images based on the quantum-behaved particle swarm optimization(QPSO) algorithm and diversity-mutation. To classify remote sensing images, we adopted unsupervised classification, and used the Gaussian distance function between the image pixels and the cluster centers as the classification standard. We used the QPSO algorithm to optimize the cluster centers. For clustering, we propose diversity-mutation to prevent premature convergence of the QPSO algorithm to optimize the classification results. The experimental results show that the proposed algorithm not only has better search speed, but also has higher convergence precision, and searches and optimizes the best cluster center more efficiently. Therefore, we conclude that the algorithm is effective and feasible.
References:
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