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Proceedings Paper

Comparison of bare soil extraction methods in black soil zone for AHSI/GF-5 remote sensing data
Author(s): Kun Shang; Chenchao Xiao; Shuneng Liang
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Paper Abstract

The black soil zone in the northeast of China is one of the three largest black soil zones in the world, and the most important cultivated area for growing food crops in China. Remote sensing can obtain regional soil information of large area more rapidly with less labor and money. One of the key issues of soil investigation is the extraction of bare soil. Hyperspectral remote sensing data have more spectral bands and nearly continuous spectral curve, indicating more detailed information of the soil properties than traditional multispectral images. By using hyperspectral data, we can obtain reliable bare soil information. This study aims to compare different bare soil extraction methods for black soil zone and analyze their feasibilities to be applied to AHSI/GF-5 data. Baoqing County in Heilongjiang Province is chosen as our study area. To perform a comprehensive and complete comparison of bare soil extraction methods, we compare 8 classical target detection algorithms and analyze the impacts of spectral dimension reduction and the spatial filter on the extraction results. The results show that it is feasible to extract bare soil information in the black soil zone based on AHSI/GF-5 hyperspectral data. MF and CEM can get the best extraction results with NPSAD and MNF through human-computer interaction parameter adjustment, while MTMF can also obtain a good extraction result without human interference.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1143219 (14 February 2020); doi: 10.1117/12.2539360
Show Author Affiliations
Kun Shang, Ministry of Natural Resources of the People's Republic of China (China)
Chenchao Xiao, Ministry of Natural Resources of the People's Republic of China (China)
Shuneng Liang, Ministry of Natural Resources of the People's Republic of China (China)


Published in SPIE Proceedings Vol. 11432:
MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Zhiguo Cao; Jie Ma; Zhong Chen; Yu Shi, Editor(s)

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