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Proceedings Paper

Band selection for hyperspectral image classification by a sliding window model
Author(s): Baofeng Guo; Yuesong Lin; Dongliang Peng; Anke Xue
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Paper Abstract

We investigate how to better use mutual information (MI) to select bands for hyperspectral image classification with less human intervention. Mutual information effectively measures the statistical dependence between two random variables. By modeling ground truth (e.g., a reference map) as one of the two random variables, MI can be used to find the spectral bands that contribute most to image classification. Extending our earlier work, we propose a sliding window model and apply mutual information to construct the estimated reference map, which need less human intervention. Experiments on the AVIRIS 92AV3C data set show that the proposed approach outperformed the benchmark methods, removing up to 55% of bands without significant loss of classification accuracy, compared to the 40% from that using the reference map accompanied with the data set. Meanwhile, its performance is found to be much robust to accuracy degradation when bands are cut off beyond 60%, revealing a better agreement in the mutual information estimation.

Paper Details

Date Published: 23 November 2011
PDF: 7 pages
Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80061G (23 November 2011); doi: 10.1117/12.902738
Show Author Affiliations
Baofeng Guo, Hangzhou Dianzi Univ. (China)
Yuesong Lin, Hangzhou Dianzi Univ. (China)
Dongliang Peng, Hangzhou Dianzi Univ. (China)
Anke Xue, Hangzhou Dianzi Univ. (China)

Published in SPIE Proceedings Vol. 8006:
MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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