Unsupervised Band Selection for Hyperspectral Datasets by Double Graph Laplacian Diagonalization

The vast amount of spectral information provided by hyperspectral images can be useful for different applications. However, the presence of redundant bands will negatively affect application performance. Therefore, it is crucial to select a relevant subset that preserves the information of the origi...

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Bibliographic Details
Main Authors: Khachatrian, Eduard, Chlaily, Saloua, Eltoft, Torbjorn, Gamba, Paolo, Marinoni, Andrea
Format: Conference Proceeding
Language:English
Subjects:
Atmospheric measurements
band selection
Clustering methods
Data analysis
Geoscience and remote sensing
hyperspectral images
Information retrieval
information theory
Laplace equations
Particle measurements
Online Access:Request full text
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Summary:The vast amount of spectral information provided by hyperspectral images can be useful for different applications. However, the presence of redundant bands will negatively affect application performance. Therefore, it is crucial to select a relevant subset that preserves the information of the original set. In this paper, we present an automatic and accurate band selection method based on Graph Laplacians. Unlike existing band selection methods, this method exploits two similarity measures simultaneously. Furthermore, it is performed on a superpixel level, so it allows us to preserve not only global but contemporaneously local particularities of original data. Experiments show the importance of measuring the relevance of the bands at local and global scales and the ability of the method to minimize intercorrelation among selected bands, hence improving the selection of the most informative spectral channels.
ISSN:2153-7003
2153-6996
2153-7003
DOI:10.1109/IGARSS47720.2021.9553127