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Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven
Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
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 …
Applications of hyperspectral imaging technology in the food industry
Emerging issues related to food quality include the need to ensure food safety, detect
adulteration and enable traceability throughout the food supply chain. Such issues must be …
adulteration and enable traceability throughout the food supply chain. Such issues must be …
Tensor methods in computer vision and deep learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
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 anomaly detection based on machine learning: An overview
Hyperspectral anomaly detection (HAD) is an important hyperspectral image application.
HAD can find pixels with anomalous spectral signatures compared with their neighbor …
HAD can find pixels with anomalous spectral signatures compared with their neighbor …
Recent advances and new guidelines on hyperspectral and multispectral image fusion
Hyperspectral image (HSI) with high spectral resolution often suffers from low spatial
resolution owing to the limitations of imaging sensors. Image fusion is an effective and …
resolution owing to the limitations of imaging sensors. Image fusion is an effective and …
Fractional Fourier image transformer for multimodal remote sensing data classification
With the recent development of the joint classification of hyperspectral image (HSI) and light
detection and ranging (LiDAR) data, deep learning methods have achieved promising …
detection and ranging (LiDAR) data, deep learning methods have achieved promising …
Prior-based tensor approximation for anomaly detection in hyperspectral imagery
The key to hyperspectral anomaly detection is to effectively distinguish anomalies from the
background, especially in the case that background is complex and anomalies are weak …
background, especially in the case that background is complex and anomalies are weak …
Spatial-spectral structured sparse low-rank representation for hyperspectral image super-resolution
Hyperspectral image super-resolution by fusing high-resolution multispectral image (HR-
MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution …
MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution …