Hyperspectral unmixing (HSU) is an essential technique that aims to address the mixed
pixels problem in hyperspectral imagery via estimating the abundance of each endmember …

The deep learning methods have started showing promising results for spectral unmixing.
We observe that many of them need direct supervision in the form of unmixed components …

Spectral unmixing plays a vital role in hyperspectral image analysis. It mainly consists of two
procedures, ie, endmember extraction and abundance estimation. Although most algorithms …

Spectral unmixing is a significant challenge in hyperspectral image processing. Existing
unmixing methods utilize prior knowledge about the abundance distribution to solve the …

Time-series remote sensing images are important in agricultural monitoring and
investigation. However, most time-series data with high temporal resolution have the …

This paper proposes a framework for unmixing of hyperspectral data that is based on
utilizing the scattering transform to extract deep features that are then used within a neural …

Surface water contamination incidents are typically caused by an enterprise's excessive or
clandestine illegal discharge. Accurate and rapid identification of pollution sources is crucial …

Spectral unmixing (SU) is a useful tool for hyperspectral remote sensing image analysis.
However, due to the interference of spectral variance and non‐linearity caused by photon …

Spectral unmixing is an important task in hyperspectral image processing for separating the
mixed spectral data pertaining to various materials observed individual pixels. Recently …

Hyperspectral unmixing is a key preprocessing technique for hyperspectral image analysis.
To further improve the unmixing performance, in this paper, a nonlocal low-rank prior …