Low-rank and sparse representation for hyperspectral image processing: A review
Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …
Hyperspectral unmixing based on nonnegative matrix factorization: A comprehensive review
Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …
endmembers and their corresponding abundances from a hyperspectral image (HSI) …
Spectral–spatial weighted sparse regression for hyperspectral image unmixing
Spectral unmixing aims at estimating the fractional abundances of a set of pure spectral
materials (endmembers) in each pixel of a hyperspectral image. The wide availability of …
materials (endmembers) in each pixel of a hyperspectral image. The wide availability of …
Hyperspectral unmixing using sparsity-constrained deep nonnegative matrix factorization with total variation
Hyperspectral unmixing is an important processing step for many hyperspectral applications,
mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation …
mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation …
Spectral-spatial hyperspectral unmixing using nonnegative matrix factorization
Remotely sensed hyperspectral images contain several bands (at about adjoining
frequencies) for a similar zone on the surface of the Earth. Hyperspectral unmixing is a …
frequencies) for a similar zone on the surface of the Earth. Hyperspectral unmixing is a …
SSCU-Net: Spatial–spectral collaborative unmixing network for hyperspectral images
Linear spectral unmixing is an essential technique in hyperspectral image (HSI) processing
and interpretation. In recent years, deep learning-based approaches have shown great …
and interpretation. In recent years, deep learning-based approaches have shown great …
Spectral–spatial joint sparse NMF for hyperspectral unmixing
L Dong, Y Yuan, X Luxs - IEEE Transactions on Geoscience …, 2020 - ieeexplore.ieee.org
The nonnegative matrix factorization (NMF) combining with spatial-spectral contextual
information is an important technique for extracting endmembers and abundances of …
information is an important technique for extracting endmembers and abundances of …
Subspace clustering constrained sparse NMF for hyperspectral unmixing
X Lu, L Dong, Y Yuan - IEEE Transactions on Geoscience and …, 2019 - ieeexplore.ieee.org
As one of the most important information of hyperspectral images (HSI), spatial information is
usually simulated with the similarity among pixels to enhance the unmixing performance of …
usually simulated with the similarity among pixels to enhance the unmixing performance of …
Robust dual spatial weighted sparse unmixing for remotely sensed hyperspectral imagery
Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing
technology, leveraging the availability of pre-existing endmember spectral libraries. In recent …
technology, leveraging the availability of pre-existing endmember spectral libraries. In recent …
A plug-and-play priors framework for hyperspectral unmixing
Spectral unmixing is a widely used technique in hyperspectral image processing and
analysis. It aims to separate mixed pixels into the component materials and their …
analysis. It aims to separate mixed pixels into the component materials and their …