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Article

Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping

1
College of Electrical and Information Engineering, Hunan University, Changsha 418002, China
2
Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, 09599 Freiberg, Germany
3
Earth Observation System and Data Center, China National Space Administration, Bejing 100048, China
4
Faculty of Electrical Engineering and Computer Science, Technical University of Berlin, 10587 Berlin, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 2903; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182903
Received: 30 July 2020 / Revised: 26 August 2020 / Accepted: 4 September 2020 / Published: 7 September 2020
Combining both spectral and spatial information with enhanced resolution provides not only elaborated qualitative information on surfacing mineralogy but also mineral interactions of abundance, mixture, and structure. This enhancement in the resolutions helps geomineralogic features such as small intrusions and mineralization become detectable. In this paper, we investigate the potential of the resolution enhancement of hyperspectral images (HSIs) with the guidance of RGB images for mineral mapping. In more detail, a novel resolution enhancement method is proposed based on component decomposition. Inspired by the principle of the intrinsic image decomposition (IID) model, the HSI is viewed as the combination of a reflectance component and an illumination component. Based on this idea, the proposed method is comprised of several steps. First, the RGB image is transformed into the luminance component, blue-difference and red-difference chroma components (YCbCr), and the luminance channel is considered as the illumination component of the HSI with an ideal high spatial resolution. Then, the reflectance component of the ideal HSI is estimated with the downsampled HSI image and the downsampled luminance channel. Finally, the HSI with high resolution can be reconstructed by utilizing the obtained illumination and the reflectance components. Experimental results verify that the fused results can successfully achieve mineral mapping, producing better results qualitatively and quantitatively over single sensor data. View Full-Text
Keywords: hyperspectral image; mineral mapping; resolution enhancement; intrinsic image decomposition hyperspectral image; mineral mapping; resolution enhancement; intrinsic image decomposition
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MDPI and ACS Style

Duan, P.; Lai, J.; Ghamisi, P.; Kang, X.; Jackisch, R.; Kang, J.; Gloaguen, R. Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping. Remote Sens. 2020, 12, 2903. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182903

AMA Style

Duan P, Lai J, Ghamisi P, Kang X, Jackisch R, Kang J, Gloaguen R. Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping. Remote Sensing. 2020; 12(18):2903. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182903

Chicago/Turabian Style

Duan, Puhong, Jibao Lai, Pedram Ghamisi, Xudong Kang, Robert Jackisch, Jian Kang, and Richard Gloaguen. 2020. "Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping" Remote Sensing 12, no. 18: 2903. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182903

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