The globe's population is increasing day by day, which causes the severe problem of
organic food for everyone. Farmers are becoming progressively conscious of the need to …

Tensor-based methods have been widely studied to attack inverse problems in
hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented …

With the maturation of satellite technology, Hyperspectral Remote Sensing (HRS) platforms
have developed from the initial ground-based and airborne platforms into spaceborne …

The low spatial resolution of hyperspectral images leads to the coexistence of multiple
ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a …

Hyperspectral unmixing is a vibrant research field that focuses on the task of decomposing
mixed pixels into a collection of pure spectral signatures, known as endmembers, along with …

Unsupervised spectral unmixing (SU) consists of representing each observed pixel as a
combination of several pure materials known as endmembers, along with their …

To mitigate the impact of mixed pixels in hyperspectral images (HSIs), substantial progress
has been made in both model-and deep-learning-based unmixing methods. However, the …

Recently, the research on nonlinear unmixing for hyperspectral images (HSIs) has received
more and more attention. However, unsupervised nonlinear unmixing methods that jointly …

Sparse unmixing (SU) has been widely applied to remotely sensed hyperspectral images
(HSIs) interpretation. Compared with traditional unmixing algorithms, SU does not need to …

Hyperspectral unmixing, which estimates end-members and their corresponding abundance
fractions simultaneously, is an important task for hyperspectral applications. In this article …