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Improving hyperspectral band selection by constructing an estimated reference map

Guo, B; Damper, RI; Gunn, SR; Nelson, JDB; (2014) Improving hyperspectral band selection by constructing an estimated reference map. Journal of Applied Remote Sensing , 8 (1) , Article 083692. 10.1117/1.JRS.8.083692. Green open access

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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.

Type: Article
Title: Improving hyperspectral band selection by constructing an estimated reference map
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/1.JRS.8.083692
Publisher version: http://dx.doi.org/10.1117/1.JRS.8.083692
Language: English
Additional information: Copyright © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/). Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1477569
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