Endmember Extraction

Endmember extraction aims to identify the constituent materials (endmembers) and their proportions (abundances) within a hyperspectral image, a crucial step in analyzing remotely sensed data. Recent research emphasizes robust methods that address endmember variability and noise, employing advanced techniques like variational inference, deep learning architectures (including autoencoders and convolutional neural networks), and graph convolutional networks to improve accuracy and efficiency. These advancements are significantly impacting various fields, including remote sensing, mineral exploration, and environmental monitoring, by enabling more precise material identification and quantitative analysis from hyperspectral imagery.