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Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model

文献类型：期刊论文

作者Huang, Zhongxin3,4; Yang, Xiaomei2,4; Liu, Yueming2,4; Wang, Zhihua2,4; Ma, Yonggang1; Jing, Haitao3; Liu, Xiaoliang2,4
刊名REMOTE SENSING
出版日期2025-03-01
卷号17期号:5页码:787
关键词cultivated land parcel pattern changes segment anything model (SAM) spatial analysis multi-scale structural similarity (MS-SSIM)
DOI10.3390/rs17050787
产权排序1
文献子类Article
英文摘要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.
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WOS关键词INDEXES ; LEVEL
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001442455200001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/213294]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Yang, Xiaomei
作者单位1.Shaanxi Datu Informat Technol Ltd Co, Chengdu 610054, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
3.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Peoples R China;
4.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
推荐引用方式
GB/T 7714		 Huang, Zhongxin,Yang, Xiaomei,Liu, Yueming,et al. Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model[J]. REMOTE SENSING,2025,17(5):787.
APA Huang, Zhongxin.,Yang, Xiaomei.,Liu, Yueming.,Wang, Zhihua.,Ma, Yonggang.,...&Liu, Xiaoliang.(2025).Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model.REMOTE SENSING,17(5),787.
MLA Huang, Zhongxin,et al."Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model".REMOTE SENSING 17.5(2025):787.

入库方式： OAI收割

来源：地理科学与资源研究所

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