A Comparative Study of Loss Functions for Hyperspectral SISR
Author:
Affiliation:
1. College of Engineering and IT, University of Dubai,UAE
2. University of Strathclyde,Department of Electronic and Electrical Engineering,UK
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9908519/9909515/09909827.pdf?arnumber=9909827
Reference23 articles.
1. Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network
2. An Efficient Residual Learning Neural Network for Hyperspectral Image Superresolution
3. Super-Resolution for Hyperspectral Remote Sensing Images Based on the 3D Attention-SRGAN Network
4. Single Hyperspectral Image Super-Resolution with Grouped Deep Recursive Residual Network
5. 3D Expansion of SRCNN for Spatial Enhancement of Hyperspectral Remote Sensing Images
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1. Bayesian Hybrid Loss for Hyperspectral SISR Using 3D Wide Residual CNN;2023 IEEE International Conference on Image Processing (ICIP);2023-10-08
2. Attention-Infused 3D-SRCNN for Hyperspectral Image Super Resolution;2023 31st European Signal Processing Conference (EUSIPCO);2023-09-04
3. Spectral-Spatial MLP Network for Hyperspectral Image Super-Resolution;Remote Sensing;2023-06-12
4. A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2023
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