A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data
Synthetic aperture radar (SAR), with its all-weather, all-time observation capabilities and unique backscattering characteristics for water bodies, has become a primary data source for flood mapping. The Huanjing-2 (HJ-2E), jointly developed by China’s Ministry of Ecology and Environment...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11079287/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849389727153651712 |
|---|---|
| author | Mingyang Song Lu Xu Nanhuanuowa Zhu Zihuan Guo Haoxuan Duan Jiahua Teng Xia Lei Lijun Zuo |
| author_facet | Mingyang Song Lu Xu Nanhuanuowa Zhu Zihuan Guo Haoxuan Duan Jiahua Teng Xia Lei Lijun Zuo |
| author_sort | Mingyang Song |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR), with its all-weather, all-time observation capabilities and unique backscattering characteristics for water bodies, has become a primary data source for flood mapping. The Huanjing-2 (HJ-2E), jointly developed by China’s Ministry of Ecology and Environment and Ministry of Emergency Management, provides abundant S-band SAR data, offering strong support for environmental disaster and emergency response. While deep learning methods are now extensively employed for SAR flood segmentation tasks, existing models still face challenges when segmenting discontinuously distributed small flooded areas under agricultural environments, including insufficient classification accuracy, poor structural integrity, and blurred boundaries. To address these issues, this study focuses on high-precision flood mapping in agricultural areas by integrating deep learning methods with HJ-2E S-band SAR data. The first S-band SAR flood segmentation dataset is constructed, derived from HJ-2E data, and includes horizontal transmit-horizontal receive (HH) and horizontal transmit-horizontal receive (HV) dual-polarization backscattering features. Subsequently, a novel deep learning model, S-band flood segmentation U-shaped network (SFSUNet), was designed for flood segmentation using S-band SAR data. SFSUNet adopts the Hiera network from the visual foundation model SAM2 as its backbone and integrates the adapter module to enable efficient cross-domain knowledge transfer from natural images to S-band SAR data. In addition, a full-level channel–spatial interaction block is designed to enhance the modeling and extraction of features for small, fragmented flood areas in agricultural regions. Finally, the effectiveness of the proposed method was validated through experiments in two agricultural flood-prone areas. Experimental results demonstrated that the proposed SFSUNet model significantly outperformed ten existing models. It achieved an intersection over union of 91.87%, outperforming the best comparative method by 3.15%, with precision and recall reaching 96.41% and 95.13%, respectively. Furthermore, experimental analysis of different polarization inputs revealed that the combination of HH and HV dual-polarization data effectively enhanced flood segmentation accuracy. In single-polarization scenarios, HV polarization exhibited superior performance to HH polarization in identifying flooded agricultural parcels. This study provides critical technical support for the application of HJ-2E data in agricultural flood monitoring and lays a foundation for future research and applications of S-band SAR. |
| format | Article |
| id | doaj-art-ffa2edbb1e1d436283c84d5b6a6c1e2c |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-ffa2edbb1e1d436283c84d5b6a6c1e2c2025-08-20T03:41:52ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118194071942410.1109/JSTARS.2025.358789611079287A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR DataMingyang Song0Lu Xu1https://orcid.org/0000-0003-2508-6800Nanhuanuowa Zhu2https://orcid.org/0009-0006-4136-5366Zihuan Guo3Haoxuan Duan4https://orcid.org/0009-0001-6975-4287Jiahua Teng5Xia Lei6Lijun Zuo7Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaSatellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaSatellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, ChinaSatellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaSynthetic aperture radar (SAR), with its all-weather, all-time observation capabilities and unique backscattering characteristics for water bodies, has become a primary data source for flood mapping. The Huanjing-2 (HJ-2E), jointly developed by China’s Ministry of Ecology and Environment and Ministry of Emergency Management, provides abundant S-band SAR data, offering strong support for environmental disaster and emergency response. While deep learning methods are now extensively employed for SAR flood segmentation tasks, existing models still face challenges when segmenting discontinuously distributed small flooded areas under agricultural environments, including insufficient classification accuracy, poor structural integrity, and blurred boundaries. To address these issues, this study focuses on high-precision flood mapping in agricultural areas by integrating deep learning methods with HJ-2E S-band SAR data. The first S-band SAR flood segmentation dataset is constructed, derived from HJ-2E data, and includes horizontal transmit-horizontal receive (HH) and horizontal transmit-horizontal receive (HV) dual-polarization backscattering features. Subsequently, a novel deep learning model, S-band flood segmentation U-shaped network (SFSUNet), was designed for flood segmentation using S-band SAR data. SFSUNet adopts the Hiera network from the visual foundation model SAM2 as its backbone and integrates the adapter module to enable efficient cross-domain knowledge transfer from natural images to S-band SAR data. In addition, a full-level channel–spatial interaction block is designed to enhance the modeling and extraction of features for small, fragmented flood areas in agricultural regions. Finally, the effectiveness of the proposed method was validated through experiments in two agricultural flood-prone areas. Experimental results demonstrated that the proposed SFSUNet model significantly outperformed ten existing models. It achieved an intersection over union of 91.87%, outperforming the best comparative method by 3.15%, with precision and recall reaching 96.41% and 95.13%, respectively. Furthermore, experimental analysis of different polarization inputs revealed that the combination of HH and HV dual-polarization data effectively enhanced flood segmentation accuracy. In single-polarization scenarios, HV polarization exhibited superior performance to HH polarization in identifying flooded agricultural parcels. This study provides critical technical support for the application of HJ-2E data in agricultural flood monitoring and lays a foundation for future research and applications of S-band SAR.https://ieeexplore.ieee.org/document/11079287/Agricultural flood mappingfragmented flood regionsHuanjing-2 (HJ-2E)S-band synthetic aperture radar (SAR)semantic segmentation |
| spellingShingle | Mingyang Song Lu Xu Nanhuanuowa Zhu Zihuan Guo Haoxuan Duan Jiahua Teng Xia Lei Lijun Zuo A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Agricultural flood mapping fragmented flood regions Huanjing-2 (HJ-2E) S-band synthetic aperture radar (SAR) semantic segmentation |
| title | A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data |
| title_full | A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data |
| title_fullStr | A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data |
| title_full_unstemmed | A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data |
| title_short | A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data |
| title_sort | fine agricultural flood segmentation model for hj 2e s band sar data |
| topic | Agricultural flood mapping fragmented flood regions Huanjing-2 (HJ-2E) S-band synthetic aperture radar (SAR) semantic segmentation |
| url | https://ieeexplore.ieee.org/document/11079287/ |
| work_keys_str_mv | AT mingyangsong afineagriculturalfloodsegmentationmodelforhj2esbandsardata AT luxu afineagriculturalfloodsegmentationmodelforhj2esbandsardata AT nanhuanuowazhu afineagriculturalfloodsegmentationmodelforhj2esbandsardata AT zihuanguo afineagriculturalfloodsegmentationmodelforhj2esbandsardata AT haoxuanduan afineagriculturalfloodsegmentationmodelforhj2esbandsardata AT jiahuateng afineagriculturalfloodsegmentationmodelforhj2esbandsardata AT xialei afineagriculturalfloodsegmentationmodelforhj2esbandsardata AT lijunzuo afineagriculturalfloodsegmentationmodelforhj2esbandsardata AT mingyangsong fineagriculturalfloodsegmentationmodelforhj2esbandsardata AT luxu fineagriculturalfloodsegmentationmodelforhj2esbandsardata AT nanhuanuowazhu fineagriculturalfloodsegmentationmodelforhj2esbandsardata AT zihuanguo fineagriculturalfloodsegmentationmodelforhj2esbandsardata AT haoxuanduan fineagriculturalfloodsegmentationmodelforhj2esbandsardata AT jiahuateng fineagriculturalfloodsegmentationmodelforhj2esbandsardata AT xialei fineagriculturalfloodsegmentationmodelforhj2esbandsardata AT lijunzuo fineagriculturalfloodsegmentationmodelforhj2esbandsardata |