Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their
corresponding abundance fractions from remotely sensed scenes. Most traditional …

Hyperspectral unmixing is to decompose the mixed pixels into pure spectral signatures
(endmembers) and their proportions (abundances). Recently, deep learning-based methods …

Autoencoders (AEs) are commonly utilized for acquiring low-dimensional data
representations and performing data reconstruction, which makes them suitable for …

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 …

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 …

Harmful algal blooms have dangerous repercussions for biodiversity, the ecosystem, and
public health. Automatic identification based on remote sensing hyperspectral image …

The availability of spectral library makes hyperspectral sparse unmixing an attractive
unmixing scheme, and the powerful feature extraction capability of deep learning meets the …

Hyperspectral unmixing (HU) has become an important technique in exploiting
hyperspectral data since it decomposes a mixed pixel into a collection of endmembers …

Retinal vessels have high curvature and diverse morphology, making them difficult to
segment, especially tiny vessels. At present, the retinal vessels are mainly annotated …

Hyperspectral Image (HSI) unmixing is a critical problem in remote sensing image
processing. It aims to estimate the pure spectral signatures and their fractional abundances …