Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection

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http://hdl.handle.net/10138/320253

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Su, P.; Tarkoma, S.; Pellikka, P.K.E. Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection. Remote Sens. 2020, 12, 3319.

Title: Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection
Author: Su, Peifeng; Tarkoma, Sasu; Pellikka, Petri K. E.
Publisher: Multidisciplinary Digital Publishing Institute
Date: 2020-10-12
URI: http://hdl.handle.net/10138/320253
Abstract: Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated. Band selection is a technique that provides clear physical-meaning results for hyperspectral dimensional reduction, alleviating the difficulty for transferring and processing hyperspectral images caused by a property of hyperspectral images: large data volumes. In this study, a simple and efficient band ranking via extended coefficient of variation (BRECV) is proposed for unsupervised hyperspectral band selection. The naive idea of the BRECV algorithm is to select bands with relatively smaller means and lager standard deviations compared to their adjacent bands. To make this simple idea into an algorithm, and inspired by coefficient of variation (CV), we constructed an extended CV matrix for every three adjacent bands to study the changes of means and standard deviations, and accordingly propose a criterion to allocate values to each band for ranking. A derived unsupervised band selection based on the same idea while using entropy is also presented. Though the underlying idea is quite simple, and both cluster and optimization methods are not used, the BRECV method acquires qualitatively the same level of classification accuracy, compared with some state-of-the-art band selection methods


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