Unsupervised Unmixing

Unsupervised unmixing aims to decompose mixed spectral signals, such as those found in hyperspectral images, into their constituent pure materials (endmembers) and their corresponding proportions (abundances) without prior knowledge of these components. Current research heavily utilizes deep learning approaches, employing convolutional autoencoders and transformer networks to model both linear and nonlinear mixing processes, often incorporating auxiliary tasks like super-resolution to improve performance. These advancements are improving the accuracy and efficiency of unmixing, with implications for various fields including remote sensing, material science, and medical imaging, where accurate material identification and quantification are crucial.