@inproceedings{b11c016cb899400cb5af0c7158c67341,
title = "Multi-feature fusion network for efficient cloud removal using SAR-optical image fusion",
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.",
keywords = "deep learning, information reconstruction, missing information, remote sensing, SAR-optical, 2024 OA procedure",
author = "Chenxi Duan and Mariana Belgiu and Alfred Stein",
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",
month = sep,
day = "5",
doi = "10.1109/IGARSS53475.2024.10641774",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "9062--9065",
booktitle = "IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}