Coupled Tensor Block Term Decomposition with Superpixel-Based Graph Laplacian Regularization for Hyperspectral Super-Resolution

Liu, Hongyi; Jiang, Wen; Zha, Yuchen; Zhang, Wei

doi:10.3390/rs14184520

Cited by 3 publications

(1 citation statement)

References 48 publications

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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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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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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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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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