Study on sophisticated vegetation classification for AHSI/GF-5 remote sensing data

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.