DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation
To address the challenge of low automation accuracy in cropland extraction caused by complex mountainous terrain, severe cropland fragmentation, and ambiguous boundaries, this study proposes a novel semantic segmentation model for cropland in high-resolution remote sensing imagery, termed detail-enh...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11072719/ |
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| author | Liang Huang Zixuan Zhang You Yu Bo-Hui Tang |
| author_facet | Liang Huang Zixuan Zhang You Yu Bo-Hui Tang |
| author_sort | Liang Huang |
| collection | DOAJ |
| description | To address the challenge of low automation accuracy in cropland extraction caused by complex mountainous terrain, severe cropland fragmentation, and ambiguous boundaries, this study proposes a novel semantic segmentation model for cropland in high-resolution remote sensing imagery, termed detail-enhanced and distance-attenuated network (DEDANet), which integrates detail enhancement and distance attenuation mechanisms. Into the encoding stage, introduced is a detail enhancement convolution module that amplifies high-frequency edge information; through a multibranch feature extraction pathway fusing five distinct convolutional types, the model significantly enhances its sensitivity and representational capacity for irregular cropland boundaries. Particularly in the decoder stage, embedded is a distance-attenuated transformer module, wherein an attenuation matrix assigns differentiated attention weights to pixels across spatial locations—thereby suppressing irrelevant background interference and reinforcing the contextual coherence of adjacent regions. By jointly optimizing and integrating global dependencies with local fine-grained details, not only are long-range contextual relationships captured, but also the precision of localized boundaries is substantially refined, leading to notable improvements in cropland boundary delineation and internal connectivity under complex terrains. To evaluate the model’s performance, a mountainous cropland dataset encompassing diverse cropland types was constructed. Experimental results demonstrate that the DEDANet surpasses mainstream models across key metrics, achieving an overall accuracy of 95.76%, an F1-score of 95.15%, and a mean intersection over union of 90.79%. Furthermore, ablation studies and cross-regional validations on the high-resolution cropland non-agriculturalization and iFLYTEK datasets verify the model’s robust generalization ability, thereby offering an efficient and scalable technical solution for refined cropland management in mountainous regions. |
| format | Article |
| id | doaj-art-b7ae75bb446e46daa1b32c1f2939a970 |
| institution | DOAJ |
| 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-b7ae75bb446e46daa1b32c1f2939a9702025-08-20T02:45:49ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118175651757910.1109/JSTARS.2025.358694811072719DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance AttenuationLiang Huang0https://orcid.org/0000-0001-6667-759XZixuan Zhang1https://orcid.org/0009-0004-1410-2654You Yu2Bo-Hui Tang3https://orcid.org/0000-0002-1918-5346Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, ChinaGeological Disaster Survey and Monitoring Institute of Hunan Province, Changsha, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, ChinaTo address the challenge of low automation accuracy in cropland extraction caused by complex mountainous terrain, severe cropland fragmentation, and ambiguous boundaries, this study proposes a novel semantic segmentation model for cropland in high-resolution remote sensing imagery, termed detail-enhanced and distance-attenuated network (DEDANet), which integrates detail enhancement and distance attenuation mechanisms. Into the encoding stage, introduced is a detail enhancement convolution module that amplifies high-frequency edge information; through a multibranch feature extraction pathway fusing five distinct convolutional types, the model significantly enhances its sensitivity and representational capacity for irregular cropland boundaries. Particularly in the decoder stage, embedded is a distance-attenuated transformer module, wherein an attenuation matrix assigns differentiated attention weights to pixels across spatial locations—thereby suppressing irrelevant background interference and reinforcing the contextual coherence of adjacent regions. By jointly optimizing and integrating global dependencies with local fine-grained details, not only are long-range contextual relationships captured, but also the precision of localized boundaries is substantially refined, leading to notable improvements in cropland boundary delineation and internal connectivity under complex terrains. To evaluate the model’s performance, a mountainous cropland dataset encompassing diverse cropland types was constructed. Experimental results demonstrate that the DEDANet surpasses mainstream models across key metrics, achieving an overall accuracy of 95.76%, an F1-score of 95.15%, and a mean intersection over union of 90.79%. Furthermore, ablation studies and cross-regional validations on the high-resolution cropland non-agriculturalization and iFLYTEK datasets verify the model’s robust generalization ability, thereby offering an efficient and scalable technical solution for refined cropland management in mountainous regions.https://ieeexplore.ieee.org/document/11072719/Cropland extractionremote sensingsemantic segmentation |
| spellingShingle | Liang Huang Zixuan Zhang You Yu Bo-Hui Tang DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Cropland extraction remote sensing semantic segmentation |
| title | DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation |
| title_full | DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation |
| title_fullStr | DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation |
| title_full_unstemmed | DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation |
| title_short | DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation |
| title_sort | dedanet mountainous cropland extraction from remote sensing imagery with detail enhancement and distance attenuation |
| topic | Cropland extraction remote sensing semantic segmentation |
| url | https://ieeexplore.ieee.org/document/11072719/ |
| work_keys_str_mv | AT lianghuang dedanetmountainouscroplandextractionfromremotesensingimagerywithdetailenhancementanddistanceattenuation AT zixuanzhang dedanetmountainouscroplandextractionfromremotesensingimagerywithdetailenhancementanddistanceattenuation AT youyu dedanetmountainouscroplandextractionfromremotesensingimagerywithdetailenhancementanddistanceattenuation AT bohuitang dedanetmountainouscroplandextractionfromremotesensingimagerywithdetailenhancementanddistanceattenuation |