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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 …
Graph representation learning meets computer vision: A survey
A graph structure is a powerful mathematical abstraction, which can not only represent
information about individuals but also capture the interactions between individuals for …
information about individuals but also capture the interactions between individuals for …
Representation-enhanced status replay network for multisource remote-sensing image classification
Deep-learning-based methods are widely used in multisource remote-sensing image
classification, and the improvement in their performance confirms the effectiveness of deep …
classification, and the improvement in their performance confirms the effectiveness of deep …
Multilayer sparsity-based tensor decomposition for low-rank tensor completion
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
Hyperspectral image super-resolution via deep spatiospectral attention convolutional neural networks
Hyperspectral images (HSIs) are of crucial importance in order to better understand features
from a large number of spectral channels. Restricted by its inner imaging mechanism, the …
from a large number of spectral channels. Restricted by its inner imaging mechanism, the …
Fusing hyperspectral and multispectral images via coupled sparse tensor factorization
Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution
multispectral image (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR …
multispectral image (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR …
Cross-scale mixing attention for multisource remote sensing data fusion and classification
Hyperspectral and multispectral images (HS/MS) fusion and classification as an important
branch of data quality improvement and interpretation have attracted increasing attention in …
branch of data quality improvement and interpretation have attracted increasing attention in …
Learning a low tensor-train rank representation for hyperspectral image super-resolution
Hyperspectral images (HSIs) with high spectral resolution only have the low spatial
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
A fast and compact 3-D CNN for hyperspectral image classification
Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI
classification (HSIC) is a challenging task due to high interclass similarity, high intraclass …
classification (HSIC) is a challenging task due to high interclass similarity, high intraclass …
SSR-NET: Spatial–spectral reconstruction network for hyperspectral and multispectral image fusion
The fusion of a low-spatial-resolution hyperspectral image (HSI)(LR-HSI) with its
corresponding high-spatial-resolution multispectral image (MSI)(HR-MSI) to reconstruct a …
corresponding high-spatial-resolution multispectral image (MSI)(HR-MSI) to reconstruct a …