Subspace-Based Preprocessing Module for Fast Hyperspectral Endmember Selection

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 into a low-dimensional data subspace using singular value decomposition. For each component pair, FSPM then detects its convex hull vertices and proposes a local outlier score measurement to remove potential outliers. FSPM finally transforms determined data points into noise-reduced data space for the sake of identifying endmembers. The proposed FSPM sheds new light on the current preprocessing field, which can fast reduce noises and outliers as well as remove redundant data points. Based on various validation metrics, experiments conducted on both synthetic and real HSIs indicate that the proposed FSPM is superior to current state-of-the-art preprocessing techniques.