Endmember optimization model of hyperspectral image based on constant constraint error

doi:10.7523/j.ucas.2020.0008

Abstract

Abstract: The linear mixture model (LMM) plays a crucial role in endmember extraction. In general, under the assumption of LMM, the endmembers in an image can be obtained by minimizing the model reconstruction error. However, due to the existence of noise, the endmembers corresponding to the minimum model reconstruction error often deviate from the real ones. In order to balance the effects of reconstruction error and noise, the geometric optimization model is adopted to evaluate the reconstruction error in this paper and the reconstruction error is used as the constraint to further minimize the volume of the simplex. The presented method is called the error invariant constrained-optimal simplex volume method (EIC-OSV). The experiments with simulated and real hyperspectral data demonstrate that EIC-OSV can improve the overall accuracy of the popular endmember extraction methods.

Key words: hyperspectral, endmember extraction, simplex, optimization

CLC Number:

TP751

WANG Weijia, GENG Xiurui. Endmember optimization model of hyperspectral image based on constant constraint error[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(1): 83-90.

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