Multi-feature fusion network for efficient cloud removal using SAR-optical image fusion

Chenxi Duan*, Mariana Belgiu, Alfred Stein

*Corresponding author for this work

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

Abstract

Clouds in optical images are inevitable and can adversely affect subsequent analysis. Given that SAR imagery remains unaffected by cloud cover, many cloud removal methods involve the fusion of SAR and optical imagery. Unfortunately, existing methods for cloud removal through SAR-optical image fusion are computationally intensive and time-consuming, limiting their practical application. To address these challenges, this paper proposes a novel multi-feature fusion network (MFFNet) for SAR-optical image fusion, aiming to remove clouds from optical images effectively. The proposed method was applied to global and all-season Sentinel-1 and Sentinel-2 images. Quantitative experiments demonstrate that MFFNet achieves high accuracy and efficiency. Specifically, our method obtains an SSIM value of 87.10 and a speed of 25.97 FPS.

Original languageEnglish
Title of host publicationIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherIEEE
Pages9062-9065
Number of pages4
ISBN (Electronic)9798350360325
DOIs
Publication statusPublished - 5 Sept 2024
Event2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece
Duration: 7 Jul 202412 Jul 2024

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Abbreviated titleIGARSS
Country/TerritoryGreece
CityAthens
Period7/07/2412/07/24

Keywords

  • deep learning
  • information reconstruction
  • missing information
  • remote sensing
  • SAR-optical
  • 2024 OA procedure

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