Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model
Change detection of cultivated land parcels is critical for achieving refined management of farmland. However, existing change detection methods based on high-resolution remote sensing imagery focus primarily on cultivation type changes, neglecting the importance of detecting parcel pattern changes....
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/787 |
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| author | Zhongxin Huang Xiaomei Yang Yueming Liu Zhihua Wang Yonggang Ma Haitao Jing Xiaoliang Liu |
| author_facet | Zhongxin Huang Xiaomei Yang Yueming Liu Zhihua Wang Yonggang Ma Haitao Jing Xiaoliang Liu |
| author_sort | Zhongxin Huang |
| collection | DOAJ |
| description | Change detection of cultivated land parcels is critical for achieving refined management of farmland. However, existing change detection methods based on high-resolution remote sensing imagery focus primarily on cultivation type changes, neglecting the importance of detecting parcel pattern changes. To address the issue of detecting diverse types of changes in cultivated land parcels, this study constructs an automated workflow framework for change detection, based on the unsupervised segmentation method of the SAM (Segment Anything Model). By performing spatial connection analysis on cultivated land parcel units extracted by the SAM for two phases and combining multiple features such as texture features (GLCM), multi-scale structural similarity (MS-SSIM), and normalized difference vegetation index (NDVI), precise identification of cultivation type and pattern change areas was achieved. The study results show that the proposed method achieved the highest accuracy in detecting parcel pattern changes in plain areas (precision: 78.79%, recall: 79.45%, IOU: 78.44%), confirming the effectiveness of the proposed method. This study provides an efficient and low-cost detection and distinction method for analyzing changes in cultivated land patterns and types using high-resolution remote sensing images, which can be directly applied in real-world scenarios. The method significantly enhances the automation and timeliness of parcel unit change detection, offering important applications for advancing precision agriculture and sustainable land resource management. |
| format | Article |
| id | doaj-art-fb63dfb016c545308a4d896e6d6366a4 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-fb63dfb016c545308a4d896e6d6366a42025-08-20T02:52:35ZengMDPI AGRemote Sensing2072-42922025-02-0117578710.3390/rs17050787Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything ModelZhongxin Huang0Xiaomei Yang1Yueming Liu2Zhihua Wang3Yonggang Ma4Haitao Jing5Xiaoliang Liu6State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaShaanxi Datu Information Technology Limited Company, Chengdu 610054, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaChange detection of cultivated land parcels is critical for achieving refined management of farmland. However, existing change detection methods based on high-resolution remote sensing imagery focus primarily on cultivation type changes, neglecting the importance of detecting parcel pattern changes. To address the issue of detecting diverse types of changes in cultivated land parcels, this study constructs an automated workflow framework for change detection, based on the unsupervised segmentation method of the SAM (Segment Anything Model). By performing spatial connection analysis on cultivated land parcel units extracted by the SAM for two phases and combining multiple features such as texture features (GLCM), multi-scale structural similarity (MS-SSIM), and normalized difference vegetation index (NDVI), precise identification of cultivation type and pattern change areas was achieved. The study results show that the proposed method achieved the highest accuracy in detecting parcel pattern changes in plain areas (precision: 78.79%, recall: 79.45%, IOU: 78.44%), confirming the effectiveness of the proposed method. This study provides an efficient and low-cost detection and distinction method for analyzing changes in cultivated land patterns and types using high-resolution remote sensing images, which can be directly applied in real-world scenarios. The method significantly enhances the automation and timeliness of parcel unit change detection, offering important applications for advancing precision agriculture and sustainable land resource management.https://www.mdpi.com/2072-4292/17/5/787cultivated land parcel pattern changessegment anything model (SAM)spatial analysismulti-scale structural similarity (MS-SSIM) |
| spellingShingle | Zhongxin Huang Xiaomei Yang Yueming Liu Zhihua Wang Yonggang Ma Haitao Jing Xiaoliang Liu Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model Remote Sensing cultivated land parcel pattern changes segment anything model (SAM) spatial analysis multi-scale structural similarity (MS-SSIM) |
| title | Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model |
| title_full | Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model |
| title_fullStr | Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model |
| title_full_unstemmed | Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model |
| title_short | Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model |
| title_sort | multi type change detection and distinction of cultivated land parcels in high resolution remote sensing images based on segment anything model |
| topic | cultivated land parcel pattern changes segment anything model (SAM) spatial analysis multi-scale structural similarity (MS-SSIM) |
| url | https://www.mdpi.com/2072-4292/17/5/787 |
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