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 …
Enhanced autoencoders with attention-embedded degradation learning for unsupervised hyperspectral image super-resolution
Recently, unmixing-based networks have shown significant potential in unsupervised
multispectral-aided hyperspectral image super-resolution (MS-aided HS-SR) task …
multispectral-aided hyperspectral image super-resolution (MS-aided HS-SR) task …
X-shaped interactive autoencoders with cross-modality mutual learning for unsupervised hyperspectral image super-resolution
Hyperspectral image super-resolution (HSI-SR) can compensate for the incompleteness of
single-sensor imaging and provide desirable products with both high spatial and spectral …
single-sensor imaging and provide desirable products with both high spatial and spectral …
Herosnet: Hyperspectral explicable reconstruction and optimal sampling deep network for snapshot compressive imaging
Hyperspectral imaging is an essential imaging modality for a wide range of applications,
especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral …
especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral …
An iterative regularization method based on tensor subspace representation for hyperspectral image super-resolution
Hyperspectral image super-resolution (HSI-SR) can be achieved by fusing a paired
multispectral image (MSI) and hyperspectral image (HSI), which is a prevalent strategy. But …
multispectral image (MSI) and hyperspectral image (HSI), which is a prevalent strategy. But …
[PDF][PDF] Hyperspectral and Multispectral Image Fusion Using Factor Smoothed Tensor Ring Decomposition.
Fusing a pair of low-spatial-resolution hyperspec-tral image (LR-HSI) and high-spatial-
resolution multispectral image (HR-MSI) has been regarded as an effective and economical …
resolution multispectral image (HR-MSI) has been regarded as an effective and economical …
A review of spatial enhancement of hyperspectral remote sensing imaging techniques
Remote sensing technology has undeniable importance in various industrial applications,
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …
Hyperspectral-multispectral image fusion via tensor ring and subspace decompositions
Fusion from a spatially low resolution hyperspectral image (LR-HSI) and a spectrally low
resolution multispectral image (MSI) to produce a high spatial-spectral HSI (HR-HSI), known …
resolution multispectral image (MSI) to produce a high spatial-spectral HSI (HR-HSI), known …
Multi-Frequency Graph Convolutional Network with Cross-Modality Mutual Enhancement for Multisource Remote Sensing Data Classification
JY Yang, HC Li, JH Yang, L Pan, Q Du… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The mining of meaningful features and effective fusion of multisource remote sensing (RS)
data have always been the challenging research problems in the joint classification of …
data have always been the challenging research problems in the joint classification of …