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Remote Sens. 2019, 11(3), 350; https://doi.org/10.3390/rs11030350

An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor

School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an 710072, China
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Received: 22 January 2019 / Revised: 6 February 2019 / Accepted: 7 February 2019 / Published: 10 February 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis. To address this issue, band selection is considered as an effective approach that removes redundant bands for HSI. Recently, many band selection methods have been proposed, but the majority of them have extremely poor accuracy in a small number of bands and require multiple iterations, which does not meet the purpose of band selection. Therefore, we propose an efficient clustering method based on shared nearest neighbor (SNNC) for hyperspectral optimal band selection, claiming the following contributions: (1) the local density of each band is obtained by shared nearest neighbor, which can more accurately reflect the local distribution characteristics; (2) in order to acquire a band subset containing a large amount of information, the information entropy is taken as one of the weight factors; (3) a method for automatically selecting the optimal band subset is designed by the slope change. The experimental results reveal that compared with other methods, the proposed method has competitive computational time and the selected bands achieve higher overall classification accuracy on different data sets, especially when the number of bands is small. View Full-Text
Keywords: Hyperspectral image (HSI); band selection; shared nearest neighbor; optimal band number Hyperspectral image (HSI); band selection; shared nearest neighbor; optimal band number
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, Q.; Wang, Q.; Li, X. An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor. Remote Sens. 2019, 11, 350.

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