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 …
A survey on hyperspectral image restoration: From the view of low-rank tensor approximation
The ability to capture fine spectral discriminative information enables hyperspectral images
(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the …
(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the …
Zero-shot hyperspectral sharpening
Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …
Coupled coarray tensor CPD for DOA estimation with coprime L-shaped array
Conventional canonical polyadic decomposition (CPD) approach for tensor-based sparse
array direction-of-arrival (DOA) estimation typically partitions the coarray statistics to …
array direction-of-arrival (DOA) estimation typically partitions the coarray statistics to …
Unsupervised 3D tensor subspace decomposition network for spatial-temporal-spectral fusion of hyperspectral and multispectral images
W Sun, K Ren, X Meng, G Yang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Due to sensor design limitations and the influence of weather factors, it is currently
challenging to obtain remote sensing images with high temporal, spatial, and spectral …
challenging to obtain remote sensing images with high temporal, spatial, and spectral …
NonRegSRNet: A nonrigid registration hyperspectral super-resolution network
Due to the limitations of imaging systems, satellite hyperspectral imagery (HSI), which yields
rich spectral information in many channels, often suffers from poor spatial resolution. HSI …
rich spectral information in many channels, often suffers from poor spatial resolution. HSI …
SSAU-Net: A spectral–spatial attention-based U-Net for hyperspectral image fusion
Compared with the traditional remote sensing image, there is a large amount of spectral
information in the hyperspectral image (HSI), which makes HSI better reflect the actual …
information in the hyperspectral image (HSI), which makes HSI better reflect the actual …
A locally optimized model for hyperspectral and multispectral images fusion
K Ren, W Sun, X Meng, G Yang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The maintenance of spectral variability between subclass objects and the relationship
between hyperspectral (HS) bands have been a fundamental but challenging problem for …
between hyperspectral (HS) bands have been a fundamental but challenging problem for …
Fusion of hyperspectral and multispectral images accounting for localized inter-image changes
The high spectral resolution of hyperspectral images (HSIs) generally comes at the expense
of low spatial resolution, which hinders the application of HSIs. Fusing an HSI and a …
of low spatial resolution, which hinders the application of HSIs. Fusing an HSI and a …
A tensor-network-based big data fusion framework for cyber–physical–social systems (CPSS)
Big data has been continuously generated from the rapidly developing of cloud/fog/edge
computing, Internet of Things (IoT) and 5G technology. This big data not only brings great …
computing, Internet of Things (IoT) and 5G technology. This big data not only brings great …