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Proceedings Paper

Study on sophisticated vegetation classification for AHSI/GF-5 remote sensing data
Author(s): Kun Shang; Yisong Xie; Hongyan Wei
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Paper Abstract

A detailed distribution map of different vegetation classes is of great importance for us to analyze the global ecosystem. Compared with traditional remote sensing data, hyperspectral remote sensing (HRS) data have hundreds of spectral bands and continuous spectral curves, showing great potential in sophisticated vegetation classification. And the AHSI (Advance Hyper-Spectral Imager) on-board GF-5 satellite has addressed the problem of lacking in satellite HRS data. According to the characteristics of AHSI data, we propose a modified sophisticated vegetation classification method by constructing and optimizing a vegetation feature set (FBS). This method takes the band quality, vegetation biochemical parameters, and neighborhood pixels’ spectral angle distance into consideration. The results show that our method can obtain better classification results than traditional methods with higher overall accuracy and less salt and pepper noise, indicating that it is feasible to distinguish different kinds of vegetation using the AHSI/GF-5 data.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114321A (14 February 2020); doi: 10.1117/12.2539369
Show Author Affiliations
Kun Shang, Ministry of Natural Resources of the People's Republic of China (China)
Yisong Xie, Institute of Remote Sensing and Digital Earth (China)
Hongyan Wei, 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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