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

Nour Aburaed, Mohammed Q. Alkhatib, Stephen Marshall, Jaime Zabalza, Hussain Al Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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).

Original languageEnglish
Title of host publication2021 4th International Conference on Signal Processing and Information Security, ICSPIS 2021
Place of PublicationNew York. N.Y.
Pages9-12
Number of pages4
ISBN (Electronic)9781665437967
DOIs
Publication statusPublished - 27 Dec 2021
Event4th International Conference on Signal Processing and Information Security, ICSPIS 2021 - Virtual, Online, United Arab Emirates
Duration: 24 Nov 202125 Nov 2021

Publication series

Name2021 4th International Conference on Signal Processing and Information Security, ICSPIS 2021

Conference

Conference4th International Conference on Signal Processing and Information Security, ICSPIS 2021
Country/TerritoryUnited Arab Emirates
CityVirtual, Online
Period24/11/2125/11/21

Keywords

  • 3D convolution
  • hyperspectral
  • remote sensing
  • single image super resolution

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