3D expansion of SRCNN for spatial enhancement of hyperspectral remote sensing images

Aburaed, Nour and Alkhatib, Mohammed Q. and Marshall, Stephen and Zabalza, Jaime and Al Ahmad, Hussain; (2021) 3D expansion of SRCNN for spatial enhancement of hyperspectral remote sensing images. In: 2021 4th International Conference on Signal Processing and Information Security, ICSPIS 2021. 2021 4th International Conference on Signal Processing and Information Security, ICSPIS 2021 . Institute of Electrical and Electronics Engineers Inc., ARE, pp. 9-12. ISBN 9781665437967 (https://doi.org/10.1109/ICSPIS53734.2021.9652420)

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Abstract

Hyperspectral Imagery (HSI) have high spectral resolution but suffer from low spatial resolution due to sensor tradeoffs. This limitation hinders utilizing the full potential of HSI. Single Image Super Resolution (SISR) techniques can be used to enhance the spatial resolution of HSI. Since these techniques rely on estimating missing information from one Low Resolution (LR) HSI, they are considered ill-posed. Furthermore, most spatial enhancement techniques cause spectral distortions in the estimated High Resolution (HR) HSI. This paper deals with the extension and modification of Convolutional Neural Networks (CNNs) to enhance HSI while preserving their spectral fidelity. The proposed method is tested, evaluated, and compared against other methodologies quantitatively using Peak Signal-to-noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and Spectral Angle Mapper (SAM).

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