Fusing Hyperspectral and Multispectral Images via Low-Rank Hankel Tensor Representation

Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

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“…They also incorporate a regularizer to model the high spectral-spatial correlations. Other examples include [132], [152]- [159], [266]- [273].…”

Section: B Method-based Fusionmentioning

confidence: 99%

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Aburaed

Alkhatib

Marshall

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Fusion methods have been widely used since the late 1990s. 5 Their success has been demonstrated in several studies that can be grouped into: Component Substitution, 6 Multi-resolution Analysis, 7 Matrix Factorization, 8 Tenosr-based, [9][10][11] Bayesian-based, 12 and Deep Convolutional Neural Networks (DCNNs). [13][14][15][16][17][18] The main advantage of Fusion methods is the ability to enhance the spatial quality of HSI beyond a scale factor of 8 with minimal spectral distortions.…”

Section: Introductionmentioning

confidence: 99%

Complex-valued neural network for hyperspectral single image super resolution

Aburaed

Alkhatib²,

Zabalza³

et al. 2023

Hyperspectral Imaging and Applications II

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Remote sensing applications are nowadays widely spread in various industrial fields, such as mineral and water exploration, geo-structural mapping, and natural hazards analysis. These applications require that the performance of image processing tasks, such as segmentation, object detection, and classification, to be of high accuracy. This can be achieved with relative ease if the given image has high spatial resolution as well as high spectral resolution. However, due to sensor limitations, spatial and spectral resolutions have an inherently inverse relationship and cannot be achieved simultaneously. Hyperspectral Images (HSI) have high spectral resolution, but suffer from low spatial resolution, which hinders utilizing them to their full potential. One of the most widely used approaches to enhance spatial resolution is Single Image Super Resolution (SISR) techniques. In the recent years, Deep Convolutional Neural Networks (DCNNs) have been widely used for HSI enhancement, as they have shown superiority over other traditional methods. Nonetheless, researches still aspire to enhance HSI quality further while overcoming common challenges, such as spectral distortions. Research has shown that properties of natural images can be easily captured using complex numbers. However, this has not been thoroughly investigated from the perspective of HSI SISR. In this paper, we propose a variation of a Complex Valued Neural Network (CVNN) architecture for HSI spatial enhancement. The benefits of approaching the problem from a frequency domain perspective will be answered and the proposed network will be compared to its real counterpart and other stateof-the-art approaches. The evaluation and comparison will be recorded qualitatively by visual comparison, and quantitatively using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Spectral Angle Mapper (SAM). The project can be access at: https://github.com/NourO93/3D_CCVNN_Hyperspectral.

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“…Fu et al [36] used convolutional neural network (CNN) denoisers [37] as priors for the coefficients of the dictionary. The aforementioned matrix-based anomaly detection methods tend to destroy the spatial structure of HSI and fail to effectively exploit the inherent spatial information [38,39]. In recent years, tensor-based methods have emerged as a promising approach to HSI anomaly detection, allowing the decomposition of HSI data into low-rank and sparse components.…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Low-Rank with Weighted Schatten p-Norm Minimization for Hyperspectral Anomaly Detection

Chen,

Wang,

Wang

et al. 2023

Remote Sensing

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Hyperspectral anomaly detection is an important unsupervised binary classification problem that aims to effectively distinguish between background and anomalies in hyperspectral images (HSIs). In recent years, methods based on low-rank tensor representations have been proposed to decompose HSIs into low-rank background and sparse anomaly tensors. However, current methods neglect the low-rank information in the spatial dimension and rely heavily on the background information contained in the dictionary. Furthermore, these algorithms show limited robustness when the dictionary information is missing or corrupted by high level noise. To address these problems, we propose a novel method called multi-dimensional low-rank (MDLR) for HSI anomaly detection. It first reconstructs three background tensors separately from three directional slices of the background tensor. Then, weighted schatten p-norm minimization is employed to enforce the low-rank constraint on the background tensor, and LF,1-norm regularization is used to describe the sparsity in the anomaly tensor. Finally, a well-designed alternating direction method of multipliers (ADMM) is employed to effectively solve the optimization problem. Extensive experiments on four real-world datasets show that our approach outperforms existing anomaly detection methods in terms of accuracy.

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Fusing Hyperspectral and Multispectral Images via Low-Rank Hankel Tensor Representation

Cited by 3 publications

References 57 publications

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Complex-valued neural network for hyperspectral single image super resolution

Multi-Dimensional Low-Rank with Weighted Schatten p-Norm Minimization for Hyperspectral Anomaly Detection

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