Super-resolution of satellite imagery using a wavelet multiscale-based deep convolutional neural network model

Aburaed, Nour; Panthakkan, Alavikunhu; Al-Saad, Mina; El, Marwa Chendeb; Mansoori, Saeed Al; Al-Ahmad, Hussain; Marshall, Stephen

doi:10.1117/12.2573991

Cited by 9 publications

(5 citation statements)

References 24 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DCNNs have recently shown great success in various image processing and computer vision applications. DCNNs have also been applied to RGB image SR and achieved promising performance [34], [274], [275]. Since the correlation between MSI and HSI is highly non-linear, DCNNs have high potential to achieve HR-HS with high accuracy if HR-RGB image is used.…”

Section: Deep Learning-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

View full text Add to dashboard Cite

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.

show abstract

Section: Deep Learning-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

View full text Add to dashboard Cite

show abstract

“…In reality, images are difficult to be registered finely due to the imaging platforms, imaging viewpoints and the influence of atmospheric turbulence as shown in Figure 1, and how to achieve image registration in the fusion process is a more realistic problem. Meanwhile, most of the previous fusion-based SR methods are based on convolutional neural networks (CNNs) such as WSRCNN (Aburaed et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Learning Multi-Modal Cross-Scale Deformable Transformer Network for Unregistered Hyperspectral Image Super-resolution

Dong,

Xu,

et al. 2024

AAAI

View full text Add to dashboard Cite

Hyperspectral image super-resolution (HSI-SR) is a technology to improve the spatial resolution of HSI. Existing fusion-based SR methods have shown great performance, but still have some problems as follows: 1) existing methods assume that the auxiliary image providing spatial information is strictly registered with the HSI, but images are difficult to be registered finely due to the shooting platforms, shooting viewpoints and the influence of atmospheric turbulence; 2) most of the methods are based on convolutional neural networks (CNNs), which is effective for local features but cannot utilize the global features. To this end, we propose a multi-modal cross-scale deformable transformer network (M2DTN) to achieve unregistered HSI-SR. Specifically, we formulate a spectrum-preserving based spatial-guided registration-SR unified model (SSRU) from the view of the realistic degradation scenarios. According to SSRU, we propose multi-modal registration deformable module (MMRD) to align features between different modalities by deformation field. In order to efficiently utilize the unique information between different modals, we design multi-scale feature transformer (MSFT) to emphasize the spatial-spectral features at different scales. In addition, we propose the cross-scale feature aggregation module (CSFA) to accurately reconstruct the HSI by aggregating feature information at different scales. Experiments show that M2DTN outperforms the-state-of-the-art HSI-SR methods. Code is obtainable at https://github.com/Jiahuiqu/M2DTN.

show abstract

“…Efforts have been exerted to enhance Panchromatic (PAN), RGB, and other Multispectral Images (MSI). [1][2][3][4] This problem is even more interesting when it comes to Hyperspectral Images (HSI) because their spatial resolution is considerably lower than MSI. In the literature, spatial enhancement of HSI methods are split into two categories; Fusion and Single Image Super Resolution (SISR).…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Super-resolution of satellite imagery using a wavelet multiscale-based deep convolutional neural network model

Cited by 9 publications

References 24 publications

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Learning Multi-Modal Cross-Scale Deformable Transformer Network for Unregistered Hyperspectral Image Super-resolution

Complex-valued neural network for hyperspectral single image super resolution

Contact Info

Product

Resources

About