Hyperspectral Unmixing

Hyperspectral unmixing aims to decompose mixed pixels in hyperspectral images into their constituent materials (endmembers) and their relative proportions (abundances). Current research heavily utilizes deep learning architectures, such as autoencoders, convolutional neural networks, and transformers, often incorporating spatial information and various regularization techniques to improve accuracy and robustness. These advancements are driving progress in diverse fields, including precision agriculture, environmental monitoring, and medical imaging, by enabling more precise material identification and quantification from hyperspectral data. The development of efficient and interpretable algorithms remains a key focus.