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Soft Computing
Published in: Soft Computing 17/2019

16-08-2018 | Methodologies and Application

A dynamic local cluster ratio-based band selection algorithm for hyperspectral images

Authors: Ronghua Shang, Yuyang Lan, Licheng Jiao, Rustam Stolkin

Published in: Soft Computing | Issue 17/2019

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Abstract

Hyperspectral band selection algorithms can save the computational costs during image restoration and analysis. This paper proposes a novel unsupervised band selection method, based on dynamic local cluster ratio (DLCR). The contributions of this paper can be summarized as follows. First, the similarity matrix is calculated in a novel way. Conventional approaches compute the matrix from the Euclidean distances between bands and are vulnerable to noise. Our proposed method can improve the robustness to such noise. Second, we propose an enhanced clustering strategy which clusters each band individually. Third, a dynamic ranking strategy is used to select bands iteratively. Bands that are highly correlated with each other will be prevented from being added to avoid redundancy. DLCR demonstrates improved performance on the Indian Pines and Pavia University data sets, when compared against other methods from the literature.
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Literature
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Metadata
Title
A dynamic local cluster ratio-based band selection algorithm for hyperspectral images
Authors
Ronghua Shang
Yuyang Lan
Licheng Jiao
Rustam Stolkin
Publication date
16-08-2018
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 17/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3464-7

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