Group endmember extraction algorithm based on Gram–Schmidt orthogonalization

Yan Zhao; Zhen Zhou; Donghui Wang

doi:10.1117/1.JRS.13.026504

10 May 2019 Group endmember extraction algorithm based on Gram–Schmidt orthogonalization

Yan Zhao, Zhen Zhou, Donghui Wang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 2, 026504 (May 2019). https://doi.org/10.1117/1.JRS.13.026504

Abstract

Endmember extraction is the key step in the mixed pixel decomposition for hyperspectral images. In view of the larger Markov property of endmember error in the sequence endmember extraction algorithm, which affects the endmember extraction accuracy, we propose an endmember extraction algorithm with three endmembers as a group based on Gram–Schmidt orthogonalization. According to the convex geometry theory, the spectral characteristic and the geometrical property of simplex in feature space have been analyzed, and the idea of group endmember extraction was introduced to reduce the Markov property of the endmember error, improving the endmember extraction accuracy accordingly. The orthogonal vector was searched by Gram–Schmidt orthogonalization and the image was projected to the orthogonal vector, so as to eliminate the effect of the extracted endmembers. The energy function was used as a measure index of the similarity for spectral vectors of different ground objects, and the measure index was used to determine the endmember. The algorithm was verified by using simulation data and real data. The experimental results indicated that the proposed algorithm may extract endmember automatically, and the corresponding endmember extraction accuracy was better than other algorithms.

Citation Download Citation

Yan Zhao, Zhen Zhou, and Donghui Wang "Group endmember extraction algorithm based on Gram–Schmidt orthogonalization," Journal of Applied Remote Sensing 13(2), 026504 (10 May 2019). https://doi.org/10.1117/1.JRS.13.026504

Received: 19 February 2019; Accepted: 16 April 2019; Published: 10 May 2019

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