Transformers have intrigued the vision research community with their state-of-the-art
performance in natural language processing. With their superior performance, transformers …

Hyperspectral unmixing (HSU) is an effective tool to ascertain the material composition of
each pixel in a hyperspectral image with typically hundreds of spectral channels. In this …

Linear spectral unmixing is an important technique in the analysis of mixed pixels in
hyperspectral images. In recent years, deep learning-based methods have been garnering …

Hyperspectral unmixing, which decomposes mixed pixels into the endmembers and
corresponding abundances, is an important image process for the further application of …

In recent years, deep learning (DL) has accelerated the development of hyperspectral image
(HSI) processing, expanding the range of applications further. As a typical model of …

Hyperspectral unmixing (HSU) is a crucial method to determine the fractional abundance of
the material (endmembers) in each pixel. Most spectral unmixing methods are affected by …

Infrared microscopy has become a significant analytical technique with a transformative
impact on various scientific disciplines. This review examines its applications in biomedical …

In recent years, deep-learning-based hyperspectral unmixing techniques have garnered
increasing attention and made significant advancements. However, relying solely on the use …

Multiobjective optimization aims at addressing the conflicting objectives, which has been
introduced to improve the performance of sparse hyperspectral unmixing. Recently …

Autoencoder unmixing is a popular deep learning-based spectral unmixing algorithm, which
decomposes the mixed pixels into pure endmembers and their fractional proportions, but the …