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Subspace-based Preprocessing Module for Fast Hyperspectral Endmember Selection
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Endmember extraction algorithms (EEAs) play a crucial role in hyperspectral image (HSI) perception, and yet they normally suffer from three flaws: 1) High computational burden, 2) weak noise robustness, and 3) high outlier sensitivity. To solve these problems, this article proposes a fast subspace-based preprocessing module, called fast subspace-based preprocessing module (FSPM), to select a high-quality data subset for subsequent endmember extraction. Specifically, FSPM first transforms an HSI
doi:10.1109/jstars.2021.3065534
fatcat:pqequk24affzvg4ybugfderzty