Spectral Unmixing

Spectral unmixing is a crucial technique in remote sensing that aims to decompose mixed pixels in hyperspectral images into their constituent materials (endmembers) and their corresponding proportions (abundances). Current research emphasizes the development of advanced algorithms, including deep learning models like autoencoders, generative adversarial networks (GANs), and convolutional neural networks (CNNs), often incorporating spatial context and leveraging techniques like tensor decomposition to improve accuracy and efficiency. These advancements are significantly impacting various fields, enabling more precise material identification and quantification in applications ranging from environmental monitoring to mineral exploration.