A Similarity-Based Ranking Method for Hyperspectral Band Selection

Band selection (BS) is a commonly used dimension reduction technique for hyperspectral images. In this article, we propose a similarity-based ranking (SR) strategy inspired by a density-based clustering algorithm. The representativeness of a band is evaluated according to its ability to become a clu...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2021-11, Vol.59 (11), p.9585-9599
Main Authors: Xu, Buyun, Li, Xihai, Hou, Weijun, Wang, Yiting, Wei, Yiwei
Format: Article
Language:English
Subjects:
Algorithms
Band selection (BS)
Banded structure
Clustering
Competitors
Dimensions
Ellipsoids
Euclidean distance
Euclidean geometry
Feature extraction
hyperspectral images (HSIs)
Hyperspectral imaging
Indexes
Machine learning
Methods
Partitioning algorithms
Ranking
Similarity
similarity measurement
Spatial data
Training
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Summary:Band selection (BS) is a commonly used dimension reduction technique for hyperspectral images. In this article, we propose a similarity-based ranking (SR) strategy inspired by a density-based clustering algorithm. The representativeness of a band is evaluated according to its ability to become a cluster center. We introduce structural similarity (SSIM) to measure the relationships between the bands. Thus, our proposed ranking-based BS method is called SR-SSIM. We picked state-of-the-art BS methods as competitors and carried out classification experiments on different data sets. The results illustrated that SR-SSIM outperformed the other methods. It is demonstrated, in this article, that the SSIM is more suitable for hyperspectral BS than the Euclidean distance since the SSIM can mine the spatial information contained in the band images. Furthermore, we discuss the application of BS methods on deep learning classifier. We found that proper preprocessing by the BS method can effectively eliminate redundant information and avoid overfitting.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3048138