作者:Wang, N (Wang, Nan)[ 1 ] ; Du, B (Du, Bo)[ 2 ] ; Zhang, LP (Zhang, Liangpei)[ 3 ] ; Zhang, LF (Zhang, Lifu)[ 1 ]
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷: 53  期: 1  页: 416-428
DOI: 10.1109/TGRS.2014.2322862
出版年: JAN 2015

摘要
Independent component analysis (ICA) has been recently applied into hyperspectral unmixing as a result of its low computation time and its ability to perform without prior information. However, when applying ICA for hyperspectral unmixing, the independence assumption in the ICA model conflicts with the abundance sum-to-one constraint and the abundance nonnegative constraint in the linear mixture model, which affects the hyperspectral unmixing accuracy. In this paper, we consider an abundance matrix composed of Np-dimensional variables, and we propose a new hyperspectral unmixing approach with an abundance characteristic-based ICA model. Two characteristics of the abundance variables are explored, and the model is constructed by these characteristics. A corresponding gradient descent algorithm is also proposed to solve the proposed objective function. Both the synthetic and real experimental results demonstrate that the proposed method performs better than the other state-of-the-art methods in abundance and endmember extraction.
通讯作者地址: Wang, N (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 2 ] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China
[ 3 ] Wuhan Univ, Remote Sensing Grp, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China