@inproceedings{f95c3de6a2374fb4baffa88c6bca5cdc,
title = "Speckle reduction in dual-polarimetric SAR images based on conditional diffusion model",
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.",
keywords = "Conditional Diffusion Model, Dual-Polarimetric, SAR Despeckling, Sentinel-1, 2025 OA procedure",
author = "Yaobin Ma and Hossein Aghababaei and Ling Chang and Pengke and Jingbo Wei",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024, IGARSS ; Conference date: 07-07-2024 Through 12-07-2024",
year = "2024",
doi = "10.1109/IGARSS53475.2024.10640404",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "11260--11263",
booktitle = "IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}