Hyperspectral change detection based on change vector analysis and spectral unmixing
ZHAO Liao-ying1, CHEN Xiao-fen1, LI Xiao-run2
1. Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou 310018, China;
2. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
A multiple changes method was proposed based on change vector analysis and spectral unmixing technology in order to solve the problem of multiple changes detection in multi-temporal hyperspectral images. The changed and unchanged pixels were distinguished by change vector analysis, and the threshold was iteratively solved by using the expection maximization (EM) algorithm. Endmembers were respectively extracted from the two multi-temporal hyperspectral images, and the abundances were solved for each changed pixel in both two images. Taking the correlation coefficient as similarity criterion, the endmembers for the two images were matched with the threshold of similarity adaptively determined according to the fine degree of the ground classes in the images, and the category code of each endmember was assigned. The considered multiple-change detection problem was solved by comparing the classes of each pixel in the changed region after assigning the class of each pixel according to the maximum abundances. The proposed approach was tested on both simulated and real multitemporal images showing multiple-change detection problems. Experimental results show that the proposed method can greatly increase the accuracy for multiple-change detection compared with the change detection method based on the spectral unmixing directly, and the running efficiency is more than doubled.
Received: 07 August 2016
Published: 27 September 2017
ZHAO Liao-ying, CHEN Xiao-fen, LI Xiao-run. Hyperspectral change detection based on change vector analysis and spectral unmixing. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(10): 1912-1919.
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