作者:Shang, K (Shang Kun)[ 1,2 ] ; Zhang, X (Zhang Xia)[ 1 ] ; Sun, YL (Sun Yan-li)[ 1,3 ] ; Zhang, LF (Zhang Li-fu)[ 1 ] ; Wang, SD (Wang Shu-dong)[ 1 ] ; Zhuang, Z (Zhuang Zhi)[ 1 ]

SPECTROSCOPY AND SPECTRAL ANALYSIS

卷: 35  期: 6  页: 1669-1676

DOI: 10.3964/3.issn.1000-0593(2015)06-1669-08

出版年: JUN 2015

摘要

There are two major problems of sophisticated vegetation classification (SVC) using hyperspectral image. Classification results using only spectral information can hardly meet the application requirements with the needed vegetation type becoming more sophisticated. And applications of classification image are also limited due to salt and pepper noise. Therefore the SVC strategy based on construction and optimization of vegetation feature band set (FBS) is proposed. Besides spectral and texture features of original image, 30 spectral indices which are sensitive to biological parameters of vegetation are added into FBS in order to improve the separability between different kinds of vegetation. And to achieve the same goal a spectral-dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed. A spatial-dimension optimization algorithm of FBS based on neighborhood pixels' spectral angle distance (NPSAD) is proposed so that detailed information can be kept during the image smoothing process. The results of SVC experiments based on airborne hyperspectral image show that the proposed method can significantly improve the accuracy of SVC so that some widespread application prospects like identification of crop species, monitoring of invasive species and precision agriculture are expectable.

通讯作者地址: Zhang, X (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] China Aero Geophys Survey & Remote Sensing Ctr La, Beijing 100083, Peoples R China

[ 3 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China