Speckle reduction in dual-polarimetric SAR images based on conditional diffusion model

Yaobin Ma*, Hossein Aghababaei, Ling Chang, Pengke, Jingbo Wei

*Corresponding author for this work

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

Abstract

Reducing speckle while preserving complex structures in images has always been a significant challenge in processing of Synthetic Aperture Radar (SAR) images. This paper proposes a new despeckling method for dual-polarimetric SAR images based on the conditional diffusion model. By explicitly learning specific distributions from the training data, this method better restores the image structures. To support this research, a VV-VH dual-polarimetric dataset is constructed using multitemporal fusion techniques with data obtained from the Sentinel-1 satellite. The proposed method is compared with five other SAR despeckling methods. The results show that this method performs better in preserving image details and effectively removing speckle. Furthermore, this paper introduces a new sampling method for SAR despeckling. Compared to the two existing methods, it achieves better despeckling results and superior structural preservation.

Original languageEnglish
Title of host publicationIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherIEEE
Pages11260-11263
Number of pages4
ISBN (Electronic)9798350360325
DOIs
Publication statusPublished - 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

  • Conditional Diffusion Model
  • Dual-Polarimetric
  • SAR Despeckling
  • Sentinel-1
  • 2025 OA procedure

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