TY - GEN
T1 - Bayesian hybrid loss for hyperspectral SISR using 3D wide residual CNN
AU - Aburaed, Nour
AU - Alkhatib, Mohammed Q.
AU - Marshall, Stephen
AU - Zabalza, Jaime
AU - Al Ahmad, Hussain
N1 - © 2023 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 - 2023/9/11
Y1 - 2023/9/11
N2 - Hyperspectral Imagery (HSI) has great importance in industrial remote sensing applications, such as geological exploration and soil mapping. HSI has high spectral resolution, which gives each object a unique spectral response, making them easily identifiable. Nonetheless, their spatial resolution is compromised due to sensor limitation, which hinders utilizing HSI to their full potential. This paper deals with the spatial enhancement of HSI using Single Image Super Resolution (SISR) approaches. One of the main challenges in this area of research is preserving the spectral signature of HSI while improving the spatial resolution simultaneously. To tackle this challenge, we propose a 3D Wide Residual Convolutional Neural Network (3D-WRCNN) model that effectively utilizes the principle of wide activation to enhance feature propagation throughout the network. Residual connections are also deployed to boost image reconstruction and information sharing between the layers to reduce overfitting. Furthermore, this study incorporates and demonstrates the usage of Bayesian-optimized hybrid loss function to further improve the performance of the 3D-WRCNN. The quantitative and qualitative evaluation indicate that the proposed approach prevails over other state-of-the-art approaches. The implementation of the proposed model is provided in this repository: https://github.com/NourO93/SISR_Library
AB - Hyperspectral Imagery (HSI) has great importance in industrial remote sensing applications, such as geological exploration and soil mapping. HSI has high spectral resolution, which gives each object a unique spectral response, making them easily identifiable. Nonetheless, their spatial resolution is compromised due to sensor limitation, which hinders utilizing HSI to their full potential. This paper deals with the spatial enhancement of HSI using Single Image Super Resolution (SISR) approaches. One of the main challenges in this area of research is preserving the spectral signature of HSI while improving the spatial resolution simultaneously. To tackle this challenge, we propose a 3D Wide Residual Convolutional Neural Network (3D-WRCNN) model that effectively utilizes the principle of wide activation to enhance feature propagation throughout the network. Residual connections are also deployed to boost image reconstruction and information sharing between the layers to reduce overfitting. Furthermore, this study incorporates and demonstrates the usage of Bayesian-optimized hybrid loss function to further improve the performance of the 3D-WRCNN. The quantitative and qualitative evaluation indicate that the proposed approach prevails over other state-of-the-art approaches. The implementation of the proposed model is provided in this repository: https://github.com/NourO93/SISR_Library
KW - hyperspectral
KW - SISR
KW - 3D CNN
KW - hybrid loss function
KW - Bayesian optimization
U2 - 10.1109/icip49359.2023.10221995
DO - 10.1109/icip49359.2023.10221995
M3 - Conference contribution book
SP - 2115
EP - 2119
BT - 2023 IEEE International Conference on Image Processing (ICIP)
PB - IEEE
CY - Piscataway, NJ
ER -