Complex-valued neural network for hyperspectral single image super resolution
Aburaed, Nour and Alkhatib, Mohammed Q. and Marshall, Stephen and Zabalza, Jaime and Ahmad, Hussain Al; Barnett, Nick J. and Gowen, Aoife A. and Liang, Haida, eds. (2023) Complex-valued neural network for hyperspectral single image super resolution. In: Proc. SPIE 12338, Hyperspectral Imaging and Applications II. Proc. of SPIE, 12338 . SPIE, GBR. ISBN 9781510657489 (https://doi.org/10.1117/12.2645086)
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Abstract
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 state-of-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).
ORCID iDs
Aburaed, Nour ORCID: https://orcid.org/0000-0002-5906-0249, Alkhatib, Mohammed Q., Marshall, Stephen ORCID: https://orcid.org/0000-0001-7079-5628, Zabalza, Jaime ORCID: https://orcid.org/0000-0002-0634-1725 and Ahmad, Hussain Al; Barnett, Nick J., Gowen, Aoife A. and Liang, Haida-
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Item type: Book Section ID code: 84599 Dates: DateEvent11 January 2023Published8 December 2022Published Online10 October 2022AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering
Science > Mathematics > Electronic computers. Computer scienceDepartment: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > Measurement Science and Enabling Technologies
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 09 Mar 2023 09:25 Last modified: 21 Dec 2024 01:07 URI: https://strathprints.strath.ac.uk/id/eprint/84599