Hyperspectral unmixing with shared endmember variability in homogeneous region

Due to different lighting conditions，complex atmospheric conditions and other factors，the spectral signatures of the same endmembers show visible differences at different locations in the image，a phenomenon known as spectral variability of endmembers. In fairly large scenarios，the variability can be large，but within moderately localised homogeneous regions，the variability tends to be small. The perturbed linear mixing model（PLMM）can mitigate the adverse effects caused by endmember variability during the unmixing process，but is less capable of handling the variability caused by scaling utility. For this reason，this paper improved the perturbed linear mixing model by introducing scaling factors to deal with the variability caused by the scaling utility，and used a super-pixel segmentation algorithm to delineate locally homogeneous regions，and then designed an algorithm of Shared Endmember Variability in Unmixing（SEVU）. Compared with algorithms such as perturbed linear mixing model，extended linear mixing model（ELMM），and other algorithms. The proposed SEVU algorithm was optimal in terms of mean Endmember Spectral Angular Distance（mSAD）and abundance Root Mean Square Error（aRMSE）on the synthetic dataset with 0. 085 5 and 0. 056 2，respectively. mSAD is optimal on the Jasper Ridge and Cuprite real datasets with 0. 060 3 and 0. 100 3，respectively. Experimental results on a synthetic dataset and two real datasets verify the effectiveness of the SEVU algorithm. © 2024 Chinese Academy of Sciences. All rights reserved.