AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR

作     者：Wang, Yuxi Zhang, Wenjuan Pan, Jie Jiang, Wen Yuan, Fangyan Zhang, Bo Yue, Xijuan Zhang, Bing 

作者机构：Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China 

出 版 物：《REMOTE SENSING》 (Remote Sens.)

年 卷 期：2025年第17卷第2期

页      面：275-275页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：the National Key R&D Program of China [2021YFB3900500, 2021YFB390050] National Key R&D Program of China [30-H30C01-9004-19/21] Airborne System under The Chinese High-resolution Earth Observation System 

主　　题：remote sensing cloud removal deep learning dataset full PolSAR 

摘      要：Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical have been proposed. Currently, the construction of multi-source cloud removal datasets typically employs single-polarization or dual-polarization backscatter SAR feature images, lacking a comprehensive description of target scattering information and polarization characteristics. This paper constructs a high-resolution remote sensing dataset, AIR-POLSAR-CR1.0, based on optical images, backscatter feature images, and polarization feature images using the fully polarimetric synthetic aperture radar (PolSAR) data. The dataset has been manually annotated to provide a foundation for subsequent analyses and processing. Finally, this study performs a performance analysis of typical cloud removal deep learning algorithms based on different categories and cloud coverage on the proposed standard dataset, serving as baseline results for this benchmark. The results of the ablation experiment also demonstrate the effectiveness of the PolSAR data. In summary, AIR-POLSAR-CR1.0 fills the gap in polarization feature images and demonstrates good adaptability for the development of deep learning algorithms.