Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory

Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a sing...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 19; no. 6; p. 1327
Main Authors:	Gao, Hongmin, Yang, Yao, Zhang, Xiaoke, Li, Chenming, Yang, Qi, Wang, Yongchang
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI 16.03.2019 MDPI AG
Subjects:	band selection dimension reduction game theory hyperspectral remote sensor data multi-objective particle swarm optimization hyperspectral remote sensor data dimension reduction multi-objective particle swarm optimization game theory band selection
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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Summary:	Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a single-target problem. In this case, the chosen optimal band combination may be unfavorable for the improvement of follow-up classification accuracy. Thus, in this work, inter-band correlation is considered as the premise, and information entropy and interclass separability are synthesized as the evaluation criterion of dimension reduction. The multi-objective particle swarm optimization algorithm is easy to implement and characterized by rapid convergence. It is adopted to search for the optimal band combination. In addition, game theory is also introduced to dimension reduction to coordinate potential conflicts when both information entropy and interclass separability are used to search for the optimal band combination. Experimental results reveal that compared with the dimensionality reduction method, which only uses information entropy or Bhattacharyya distance as the evaluation criterion, and the method combining multiple criterions into one by weighting, the proposed method achieves global optimum more easily, and then obtains a better band combination and possess higher classification accuracy.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The author contributed equally to this work and should be considered co-first author.
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s19061327