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Journal of Applied Remote Sensing • Open Access

Improving hyperspectral band selection by constructing an estimated reference map
Author(s): Baofeng Guo; Robert I. Damper; Steven R. Gunn; James D. B. Nelson

Paper Abstract

We investigate band selection for hyperspectral image classification. Mutual information (MI) measures the statistical dependence between two random variables. By modeling the reference map as one of the two random variables, MI can, therefore, be used to select the bands that are more useful for image classification. A new method is proposed to estimate the MI using an optimally constructed reference map, reducing reliance on ground-truth information. To reduce the interferences from noise and clutters, the reference map is constructed by averaging a subset of spectral bands that are chosen with the best capability to approximate the ground truth. To automatically find these bands, we develop a searching strategy consisting of differentiable MI, gradient ascending algorithm, and random-start optimization. Experiments on AVIRIS 92AV3C dataset and Pavia University scene dataset show that the proposed method outperformed the benchmark methods. In AVIRIS 92AV3C dataset, up to 55% of bands can be removed without significant loss of classification accuracy, compared to the 40% from that using the reference map accompanied with the dataset. Meanwhile, its performance is much more robust to accuracy degradation when bands are cut off beyond 60%, revealing a better agreement in the MI calculation. In Pavia University scene dataset, using 45 bands achieved 86.18% classification accuracy, which is only 1.5% lower than that using all the 103 bands.

Paper Details

Date Published: 17 January 2014
PDF: 16 pages
J. Appl. Remote Sens. 8(1) 083692 doi: 10.1117/1.JRS.8.083692
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Baofeng Guo, Hangzhou Dianzi Univ. (China)
Robert I. Damper, Univ. of Southampton (United Kingdom)
Steven R. Gunn, Univ. of Southampton (United Kingdom)
James D. B. Nelson, Univ. of Southampton (United Kingdom)


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