Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition
Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SA...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/23/5619 |
| _version_ | 1797399620706369536 |
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| author | Bin Pan Xianjian Shi |
| author_facet | Bin Pan Xianjian Shi |
| author_sort | Bin Pan |
| collection | DOAJ |
| description | Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines ascending and descending time-series information while considering polarization channel details to enhance the accuracy of landslide identification. The results demonstrate notable improvements in landslide recognition accuracy using the ascending and descending fusion strategy compared to single-orbit data, with F1 scores increasing by 5.19% and 8.82% in Hokkaido and Papua New Guinea, respectively. Additionally, utilizing time-series imagery in Group 2 as opposed to using only pre- and post-event images in Group 4 leads to F1 score improvements of 6.94% and 9.23% in Hokkaido and Papua New Guinea, respectively, confirming the effectiveness of time-series information in enhancing landslide recognition accuracy. Furthermore, employing dual-polarization strategies in Group 4 relative to single-polarization Groups 5 and 6 results in peak F1 score increases of 7.46% and 12.07% in Hokkaido and Papua New Guinea, respectively, demonstrating the feasibility of dual-polarization strategies. However, due to limitations in Sentinel-1 imagery resolution and terrain complexities, omissions and false alarms may arise near landslide edges. The improvements achieved in this study hold critical implications for landslide disaster assessment and provide valuable insights for further enhancing landslide recognition capabilities. |
| first_indexed | 2024-03-09T01:42:45Z |
| format | Article |
| id | doaj.art-18e5e0a5390c43fda6394c08449eb1ed |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T01:42:45Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-18e5e0a5390c43fda6394c08449eb1ed2023-12-08T15:25:15ZengMDPI AGRemote Sensing2072-42922023-12-011523561910.3390/rs15235619Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide RecognitionBin Pan0Xianjian Shi1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaConducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines ascending and descending time-series information while considering polarization channel details to enhance the accuracy of landslide identification. The results demonstrate notable improvements in landslide recognition accuracy using the ascending and descending fusion strategy compared to single-orbit data, with F1 scores increasing by 5.19% and 8.82% in Hokkaido and Papua New Guinea, respectively. Additionally, utilizing time-series imagery in Group 2 as opposed to using only pre- and post-event images in Group 4 leads to F1 score improvements of 6.94% and 9.23% in Hokkaido and Papua New Guinea, respectively, confirming the effectiveness of time-series information in enhancing landslide recognition accuracy. Furthermore, employing dual-polarization strategies in Group 4 relative to single-polarization Groups 5 and 6 results in peak F1 score increases of 7.46% and 12.07% in Hokkaido and Papua New Guinea, respectively, demonstrating the feasibility of dual-polarization strategies. However, due to limitations in Sentinel-1 imagery resolution and terrain complexities, omissions and false alarms may arise near landslide edges. The improvements achieved in this study hold critical implications for landslide disaster assessment and provide valuable insights for further enhancing landslide recognition capabilities.https://www.mdpi.com/2072-4292/15/23/5619landslidetime-seriesSARdual-polarizeddisaster assessment |
| spellingShingle | Bin Pan Xianjian Shi Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition Remote Sensing landslide time-series SAR dual-polarized disaster assessment |
| title | Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition |
| title_full | Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition |
| title_fullStr | Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition |
| title_full_unstemmed | Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition |
| title_short | Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition |
| title_sort | fusing ascending and descending time series sar images with dual polarized pixel attention unet for landslide recognition |
| topic | landslide time-series SAR dual-polarized disaster assessment |
| url | https://www.mdpi.com/2072-4292/15/23/5619 |
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