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Segment Anything Model Combined with Multi-Scale Segmentation for Extracting Complex Cultivated Land Parcels in High-Resolution Remote Sensing Images

文献类型：期刊论文

作者Huang, Zhongxin; Jing, Haitao; Liu, Yueming2,3; Yang, Xiaomei2,3; Wang, Zhihua2,3; Liu, Xiaoliang2,3; Gao, Ku2,3; Luo, Haofeng
刊名REMOTE SENSING
出版日期2024-09-01
卷号16期号:18页码:3489
关键词Segment Anything Model (SAM) multi-scale segmentation precise parcel extraction complex cultivated land
DOI10.3390/rs16183489
产权排序2
文献子类Article
英文摘要Accurate cultivated land parcel data are an essential analytical unit for further agricultural monitoring, yield estimation, and precision agriculture management. However, the high degree of landscape fragmentation and the irregular shapes of cultivated land parcels, influenced by topography and human activities, limit the effectiveness of parcel extraction. The visual semantic segmentation model based on the Segment Anything Model (SAM) provides opportunities for extracting multi-form cultivated land parcels from high-resolution images; however, the performance of the SAM in extracting cultivated land parcels requires further exploration. To address the difficulty in obtaining parcel extraction that closely matches the true boundaries of complex large-area cultivated land parcels, this study used segmentation patches with cultivated land boundary information obtained from SAM unsupervised segmentation as constraints, which were then incorporated into the subsequent multi-scale segmentation. A combined method of SAM unsupervised segmentation and multi-scale segmentation was proposed, and it was evaluated in different cultivated land scenarios. In plain areas, the precision, recall, and IoU for cultivated land parcel extraction improved by 6.57%, 10.28%, and 9.82%, respectively, compared to basic SAM extraction, confirming the effectiveness of the proposed method. In comparison to basic SAM unsupervised segmentation and point-prompt SAM conditional segmentation, the SAM unsupervised segmentation combined with multi-scale segmentation achieved considerable improvements in extracting complex cultivated land parcels. This study confirms that, under zero-shot and unsupervised conditions, the SAM unsupervised segmentation combined with the multi-scale segmentation method demonstrates strong cross-region and cross-data source transferability and effectiveness for extracting complex cultivated land parcels across large areas.
WOS关键词CROP FIELD EXTRACTION ; SCALE PARAMETER ; NEURAL-NETWORK ; VARIABILITY
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:001323604900001
源URL[http://ir.igsnrr.ac.cn/handle/311030/208018]  
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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Zhongxin,Jing, Haitao,Liu, Yueming,et al. Segment Anything Model Combined with Multi-Scale Segmentation for Extracting Complex Cultivated Land Parcels in High-Resolution Remote Sensing Images[J]. REMOTE SENSING,2024,16(18):3489.
APA Huang, Zhongxin.,Jing, Haitao.,Liu, Yueming.,Yang, Xiaomei.,Wang, Zhihua.,...&Luo, Haofeng.(2024).Segment Anything Model Combined with Multi-Scale Segmentation for Extracting Complex Cultivated Land Parcels in High-Resolution Remote Sensing Images.REMOTE SENSING,16(18),3489.
MLA Huang, Zhongxin,et al."Segment Anything Model Combined with Multi-Scale Segmentation for Extracting Complex Cultivated Land Parcels in High-Resolution Remote Sensing Images".REMOTE SENSING 16.18(2024):3489.

入库方式： OAI收割

来源：地理科学与资源研究所

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