AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR
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 dataset...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/2/275 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587579647590400 |
|---|---|
| author | Yuxi Wang Wenjuan Zhang Jie Pan Wen Jiang Fangyan Yuan Bo Zhang Xijuan Yue Bing Zhang |
| author_facet | Yuxi Wang Wenjuan Zhang Jie Pan Wen Jiang Fangyan Yuan Bo Zhang Xijuan Yue Bing Zhang |
| author_sort | Yuxi Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8137cfb39fc34b0e969bdb828fdc3ff8 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-8137cfb39fc34b0e969bdb828fdc3ff82025-01-24T13:47:58ZengMDPI AGRemote Sensing2072-42922025-01-0117227510.3390/rs17020275AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SARYuxi Wang0Wenjuan Zhang1Jie Pan2Wen Jiang3Fangyan Yuan4Bo Zhang5Xijuan Yue6Bing Zhang7Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaDue 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.https://www.mdpi.com/2072-4292/17/2/275remote sensingcloud removaldeep learningdatasetfull PolSAR |
| spellingShingle | Yuxi Wang Wenjuan Zhang Jie Pan Wen Jiang Fangyan Yuan Bo Zhang Xijuan Yue Bing Zhang AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR Remote Sensing remote sensing cloud removal deep learning dataset full PolSAR |
| title | AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR |
| title_full | AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR |
| title_fullStr | AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR |
| title_full_unstemmed | AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR |
| title_short | AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR |
| title_sort | air polsar cr1 0 a benchmark dataset for cloud removal in high resolution optical remote sensing images with fully polarized sar |
| topic | remote sensing cloud removal deep learning dataset full PolSAR |
| url | https://www.mdpi.com/2072-4292/17/2/275 |
| work_keys_str_mv | AT yuxiwang airpolsarcr10abenchmarkdatasetforcloudremovalinhighresolutionopticalremotesensingimageswithfullypolarizedsar AT wenjuanzhang airpolsarcr10abenchmarkdatasetforcloudremovalinhighresolutionopticalremotesensingimageswithfullypolarizedsar AT jiepan airpolsarcr10abenchmarkdatasetforcloudremovalinhighresolutionopticalremotesensingimageswithfullypolarizedsar AT wenjiang airpolsarcr10abenchmarkdatasetforcloudremovalinhighresolutionopticalremotesensingimageswithfullypolarizedsar AT fangyanyuan airpolsarcr10abenchmarkdatasetforcloudremovalinhighresolutionopticalremotesensingimageswithfullypolarizedsar AT bozhang airpolsarcr10abenchmarkdatasetforcloudremovalinhighresolutionopticalremotesensingimageswithfullypolarizedsar AT xijuanyue airpolsarcr10abenchmarkdatasetforcloudremovalinhighresolutionopticalremotesensingimageswithfullypolarizedsar AT bingzhang airpolsarcr10abenchmarkdatasetforcloudremovalinhighresolutionopticalremotesensingimageswithfullypolarizedsar |