[1]WANG Liguo,ZHAO Liang,SHI Yao.Maximin distance algorithm-based band selection for hyperspectral imagery[J].CAAI Transactions on Intelligent Systems,2018,13(1):131-137.[doi:10.11992/tis.201703023]
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Maximin distance algorithm-based band selection for hyperspectral imagery

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 1 Page number: 131-137 Column: 学术论文—机器感知与模式识别 Public date: 2018-01-24
Title:
Maximin distance algorithm-based band selection for hyperspectral imagery
Author(s):
WANG Liguo;  ZHAO Liang;  SHI Yao
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
hyperspectral images; band selection; band clustering; unsupervised; maximin distance; K-medoids clustering; maximum likelihood method; classification
CLC:
TN911.73;TP391
DOI:
10.11992/tis.201703023
Abstract:
In this paper, we propose a hyperspectral-image band-selection algorithm based on the maximin distance to reduce the spectral redundancy of hyperspectral remote sensing images as well as the associated computational complexity. First, the algorithm computes the standard deviation of all bands and selects the one with the maximum standard deviation as the initial center. Then, to cluster the bands, we use the maximin distance algorithm to obtain centers that are relatively farther away. Finally, we use the k-medoids algorithm to update the clustering center. The experimental results show that the bands selected by the maximin distance algorithm can satisfy the demands associated with a large amount of information and relatively low correlation. At the same time, when the obtained bands are combined for hyperspectral image classification, higher classification accuracy can be achieved.
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