Hyperspectral Band Selection for Lithologic Discrimination and Geological Mapping

Classification techniques applied to hyperspectral images are very useful for lithologic discrimination and geological mapping. Classifiers are often applied either to all spectral channels or only to absorption spectral channels. However, it is difficult to obtain different lithology information us...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.471-486
Main Authors: Tan, Yulei, Lu, Laijun, Bruzzone, Lorenzo, Guan, Renchu, Chang, Zhiyong, Yang, Chen
Format: Article
Language:English
Subjects:
Absorption
Algorithms
Band selection (BS)
Band spectra
Bandwidths
Channels
Clustering
Discrimination
Earth
Geologic mapping
Geological mapping
Homogeneity
hyperspectral imagery (HSIs)
Hyperspectral imaging
Image classification
Imaging techniques
Infrared imaging
Infrared spectrometers
lithologic discrimination
Lithology
Mapping
Minerals
remote sensing
Rock
Rocks
Spectral bands
superpixels
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Summary:Classification techniques applied to hyperspectral images are very useful for lithologic discrimination and geological mapping. Classifiers are often applied either to all spectral channels or only to absorption spectral channels. However, it is difficult to obtain different lithology information using specific absorption regions from the narrow bandwidth and contiguous spectral channels due to spectral variability among rocks. In this article, we propose a band selection (BS) method for hyperspectral lithologic discrimination, in which the lithological superpixels are first gathered. A spectral bands selection criterion is learned by measuring the homogeneity and the variation of the lithological superpixels, and lithologic discriminating bands are identified by an efficient clustering algorithm based on affinity propagation. In this article, two geologic test sites, i.e., the Airborne Visible/Infrared Imaging Spectrometer data of the Cuprite, Nevada, USA, including 11 lithologic units (9 types of rocks) and the Hyperion data of Junggar, China, with 5 lithologic units, are chosen for validation. The performance of the proposed BS method is compared with those of using all the bands, specific absorption spectral channels, and two literature BS techniques. Experimental results show that the proposed method improves mapping accuracy by selecting fewer bands with higher lithologic discrimination capability than the other considered methods.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2020.2964000