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29 October 2019 Identification of iron-bearing minerals based on HySpex hyperspectral remote sensing data
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Abstract

Our research group built a super-low altitude detection platform with power delta wings and mounted it with a HySpex hyperspectral sensor. This platform has great potential for extracting alteration information as it can provide high spectral and spatial resolution images. The aim of this study is to verify the reliability and accuracy of alteration information extraction using this platform. The seven-scene hyperspectral images of the Karatagh region in Hami of Xinjiang obtained by the platform in September 2017 were used to identify iron-bearing minerals. First, these images were preprocessed by splicing, atmospheric correction, and geometric correction. Second, the images were compared with the United States Geological Survey (USGS) standard spectra of similarities between the end-member spectra of goethite-, limonite-, and jarosite-altered minerals. Third, the three altered minerals were mapped in the test area based on the end-member spectrum. The results show that the correlations between the end-member spectra of three altered minerals, including goethite, limonite, and jarosite, obtained from the HySpex image and corresponding spectra in USGS reach 0.92873, 0.95098, and 0.8875, respectively. The end-member spectral reflectance values were higher than those in the spectral library. The original spectral data after the continuum-removal and the local quantitative display show that the spectrum at the feature absorption location was similar to the reference spectrum. On this basis, the distribution maps of goethite, limonite, and jarosite iron-stained altered minerals in the Hami Gobi area were produced and field verified. The data show that the information of identified minerals from the HySpex hyperspectral data were in accordance with the actual situation. Both methods can recognize jarosite well, the spectral angular mapping method is better than the back propagation (BP) neural network for goethite recognition, and the BP neural network of limonite recognition is better than the spectral angular mapping method. The above results prove that the alteration information can be effectively identified using the HySpex hyperspectral data integrated on a super-low altitude detection platform, which is of great significance to the rapid delineation of minerogenic prospects area.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$28.00 © 2019 SPIE
Guo Jiang, Kefa Zhou, Jinlin Wang, Shichao Cui, Shuguang Zhou, and Chao Tang "Identification of iron-bearing minerals based on HySpex hyperspectral remote sensing data," Journal of Applied Remote Sensing 13(4), 047501 (29 October 2019). https://doi.org/10.1117/1.JRS.13.047501
Received: 22 July 2019; Accepted: 10 October 2019; Published: 29 October 2019
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