Tensor decompositions for hyperspectral data processing in remote sensing: A comprehensive review
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …
(RS) imaging has provided a significant amount of spatial and spectral information for the …
Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling
The compressive sensing (CS) scheme exploits many fewer measurements than suggested
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
SpectralFormer: Rethinking hyperspectral image classification with transformers
Hyperspectral (HS) images are characterized by approximately contiguous spectral
information, enabling the fine identification of materials by capturing subtle spectral …
information, enabling the fine identification of materials by capturing subtle spectral …
Hyperspectral image transformer classification networks
Hyperspectral image (HSI) classification is an important task in earth observation missions.
Convolution neural networks (CNNs) with the powerful ability of feature extraction have …
Convolution neural networks (CNNs) with the powerful ability of feature extraction have …
Orthogonal subspace unmixing to address spectral variability for hyperspectral image
Hyperspectral unmixing aims at estimating pure spectral signatures and their proportions in
each pixel. In practice, the atmospheric effects, intrinsic variation of the spectral signatures of …
each pixel. In practice, the atmospheric effects, intrinsic variation of the spectral signatures of …
A trainable spectral-spatial sparse coding model for hyperspectral image restoration
Hyperspectral imaging offers new perspectives for diverse applications, ranging from the
monitoring of the environment using airborne or satellite remote sensing, precision farming …
monitoring of the environment using airborne or satellite remote sensing, precision farming …
HLRTF: Hierarchical low-rank tensor factorization for inverse problems in multi-dimensional imaging
Inverse problems in multi-dimensional imaging, eg, completion, denoising, and compressive
sensing, are challenging owing to the big volume of the data and the inherent ill-posedness …
sensing, are challenging owing to the big volume of the data and the inherent ill-posedness …
Tensor Low-Rank Constraint and Total Variation for Hyperspectral Image Mixed Noise Removal
M Wang, Q Wang, J Chanussot - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
Several methods based on Total Variation (TV) have been proposed for Hyperspectral
Image (HSI) denoising. However, the TV terms of these methods just use various l 1 norms …
Image (HSI) denoising. However, the TV terms of these methods just use various l 1 norms …
Hyperspectral sparse unmixing via nonconvex shrinkage penalties
Hyperspectral sparse unmixing aims at finding the optimal subset of spectral signatures in
the given spectral library and estimating their proportions in each pixel. Recently …
the given spectral library and estimating their proportions in each pixel. Recently …
Nonlocal spatial–spectral neural network for hyperspectral image denoising
Hyperspectral image (HSI) denoising is an essential preprocessing step to improve the
quality of HSIs. The difficulty of HSI denoising lies in effectively modeling the intrinsic …
quality of HSIs. The difficulty of HSI denoising lies in effectively modeling the intrinsic …