Hyperspectral Band Selection: A Review

A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational...

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Veröffentlicht in:IEEE geoscience and remote sensing magazine Jg. 7; H. 2; S. 118 - 139
Hauptverfasser: Sun, Weiwei, Du, Qian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2019
Schlagworte:
Classification algorithms
Clustering algorithms
Feature extraction
Hyperspectral imaging
Principal component analysis
ISSN:2473-2397, 2168-6831
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Zusammenfassung:A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational costs while preserving the significant spectral information of ground objects. In this article, we review current hyperspectral band selection methods, which can be classified into six main categories: ranking based, searching based, clustering based, sparsity based, embedding-learning based, and hybrid-scheme based. With two widely used hyperspectral data sets, we illustrate the classification performances of several popular band selection methods. The challenges and research directions of hyperspectral band selection are also discussed.
ISSN:2473-2397
2168-6831
DOI:10.1109/MGRS.2019.2911100