TY - GEN
T1 - 3D expansion of SRCNN for spatial enhancement of hyperspectral remote sensing images
AU - Aburaed, Nour
AU - Alkhatib, Mohammed Q.
AU - Marshall, Stephen
AU - Zabalza, Jaime
AU - Al Ahmad, Hussain
N1 - © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2021/12/27
Y1 - 2021/12/27
N2 - 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).
AB - 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).
KW - 3D convolution
KW - hyperspectral
KW - remote sensing
KW - single image super resolution
UR - http://www.scopus.com/inward/record.url?scp=85124407138&partnerID=8YFLogxK
U2 - 10.1109/ICSPIS53734.2021.9652420
DO - 10.1109/ICSPIS53734.2021.9652420
M3 - Conference contribution book
AN - SCOPUS:85124407138
T3 - 2021 4th International Conference on Signal Processing and Information Security, ICSPIS 2021
SP - 9
EP - 12
BT - 2021 4th International Conference on Signal Processing and Information Security, ICSPIS 2021
CY - New York. N.Y.
T2 - 4th International Conference on Signal Processing and Information Security, ICSPIS 2021
Y2 - 24 November 2021 through 25 November 2021
ER -