Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature Extraction
The accurate acquisition of agricultural parcels from remote sensing images is crucial for agricultural management and crop production monitoring. Most of the existing agricultural parcel extraction methods comprise semantic segmentation through remote sensing images, pixel-level classification, and...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/15/2556 |
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| author | Feiyu Teng Ling Wu Shukuan Liu |
| author_facet | Feiyu Teng Ling Wu Shukuan Liu |
| author_sort | Feiyu Teng |
| collection | DOAJ |
| description | The accurate acquisition of agricultural parcels from remote sensing images is crucial for agricultural management and crop production monitoring. Most of the existing agricultural parcel extraction methods comprise semantic segmentation through remote sensing images, pixel-level classification, and then vectorized raster data. However, this approach faces challenges such as internal cavities, unclosed boundaries, and fuzzy edges, which hinder the accurate extraction of complete agricultural parcels. Therefore, this paper proposes a vector contour segmentation network based on the hybrid backbone and multiscale edge feature extraction module (HEVNet). We use the extraction of vector polygons of agricultural parcels by predicting the location of contour points, which avoids the above problems that may occur when raster data is converted to vector data. Simultaneously, this paper proposes a hybrid backbone for feature extraction. A hybrid backbone combines the respective advantages of the Resnet and Transformer backbone networks to balance local features and global features in feature extraction. In addition, we propose a multiscale edge feature extraction module, which can extract and enhance the edge features of different scales to prevent the possible loss of edge details in down sampling. This paper uses the datasets of Denmark, the Netherlands, iFLYTEK, and Hengyang in China to evaluate our model. The obtained IOU indexes were 67.92%, 81.35%, 78.02%, and 66.35%, which are higher than previous IOU indexes based on the optimal model (DBBANet). The results demonstrate that the proposed model significantly enhances the integrity and edge accuracy of agricultural parcel extraction. |
| format | Article |
| id | doaj-art-cf51dd79ac0f4e28a49307575ee326d8 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-cf51dd79ac0f4e28a49307575ee326d82025-08-20T04:00:51ZengMDPI AGRemote Sensing2072-42922025-07-011715255610.3390/rs17152556Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature ExtractionFeiyu Teng0Ling Wu1Shukuan Liu2School of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaThe accurate acquisition of agricultural parcels from remote sensing images is crucial for agricultural management and crop production monitoring. Most of the existing agricultural parcel extraction methods comprise semantic segmentation through remote sensing images, pixel-level classification, and then vectorized raster data. However, this approach faces challenges such as internal cavities, unclosed boundaries, and fuzzy edges, which hinder the accurate extraction of complete agricultural parcels. Therefore, this paper proposes a vector contour segmentation network based on the hybrid backbone and multiscale edge feature extraction module (HEVNet). We use the extraction of vector polygons of agricultural parcels by predicting the location of contour points, which avoids the above problems that may occur when raster data is converted to vector data. Simultaneously, this paper proposes a hybrid backbone for feature extraction. A hybrid backbone combines the respective advantages of the Resnet and Transformer backbone networks to balance local features and global features in feature extraction. In addition, we propose a multiscale edge feature extraction module, which can extract and enhance the edge features of different scales to prevent the possible loss of edge details in down sampling. This paper uses the datasets of Denmark, the Netherlands, iFLYTEK, and Hengyang in China to evaluate our model. The obtained IOU indexes were 67.92%, 81.35%, 78.02%, and 66.35%, which are higher than previous IOU indexes based on the optimal model (DBBANet). The results demonstrate that the proposed model significantly enhances the integrity and edge accuracy of agricultural parcel extraction.https://www.mdpi.com/2072-4292/17/15/2556remote sensingcontour segmentationagricultural parcels extractionedge enhancement |
| spellingShingle | Feiyu Teng Ling Wu Shukuan Liu Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature Extraction Remote Sensing remote sensing contour segmentation agricultural parcels extraction edge enhancement |
| title | Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature Extraction |
| title_full | Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature Extraction |
| title_fullStr | Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature Extraction |
| title_full_unstemmed | Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature Extraction |
| title_short | Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature Extraction |
| title_sort | extraction of agricultural parcels using vector contour segmentation network with hybrid backbone and multiscale edge feature extraction |
| topic | remote sensing contour segmentation agricultural parcels extraction edge enhancement |
| url | https://www.mdpi.com/2072-4292/17/15/2556 |
| work_keys_str_mv | AT feiyuteng extractionofagriculturalparcelsusingvectorcontoursegmentationnetworkwithhybridbackboneandmultiscaleedgefeatureextraction AT lingwu extractionofagriculturalparcelsusingvectorcontoursegmentationnetworkwithhybridbackboneandmultiscaleedgefeatureextraction AT shukuanliu extractionofagriculturalparcelsusingvectorcontoursegmentationnetworkwithhybridbackboneandmultiscaleedgefeatureextraction |