Paper Info
Title
An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor.
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.
Year
DOI
Venue
2019
10.3390/rs11030350
REMOTE SENSING
Keywords
Field
DocType
Hyperspectral image (HSI),band selection,shared nearest neighbor,optimal band number
k-nearest neighbors algorithm,Computer vision,Band selection,Pattern recognition,Hyperspectral imaging,Artificial intelligence,Cluster analysis,Geology
Journal
Volume
Issue
ISSN
11
3
2072-4292
Citations 
PageRank 
References 
2
0.36
20
Authors
3
Name
Order
Citations
PageRank
Qiang Li18419.63
Qi Wang287057.63
Xuelong Li315049617.31