Sure-Ergas: Unsupervised Deep Learning Multispectral and Hyperspectral Image Fusion

Han V. Nguyen*, Magnus O. Ulfarsson, Johannes R. Sveinsson, Mauro Dalla Mura

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a new loss function to train a convolutional neural network (CNN) for multispectral and hyper-spectral (MS-HS) image fusion. The loss function is based on the relative dimensionless global error synthesis (ER-GAS), where we exchange the mean squared error (MSE) for its unbiased estimate using Stein's risk unbiased estimate (SURE). The loss function has a good balance between the spectral and spatial information implied by the weighted MSE, therefore it does not need a parameter to balance the spectral and spatial terms as in MSE loss function, and it also converges faster than the MSE one. Additionally, the loss function enables unsupervised training and avoids overfit-ting, since it is derived by using SURE. Experimental results show that the proposed method yields good results and outperforms the competitive methods. Codes are available at https://github.com/hvn2/SURE-ERGAS

Original languageEnglish
Title of host publicationIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5623-5626
Number of pages4
ISBN (Electronic)9798350320107
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States
Duration: 16 Jul 202321 Jul 2023

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2023-July

Conference

Conference2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Country/TerritoryUnited States
CityPasadena
Period16/07/2321/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Other keywords

  • ERGAS
  • Hyperspectral and multispectral image fusion
  • Stein's unbiased risk estimate (SURE)
  • unsupervised CNN

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