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

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 18; p. 3489
• Main Authors: Huang, Zhongxin, Jing, Haitao, Liu, Yueming, Yang, Xiaomei, Wang, Zhihua, Liu, Xiaoliang, Gao, Ku, Luo, Haofeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 20.09.2024
• Subjects: Accuracy; Agricultural management; Agriculture; City county; complex cultivated land; Cultivated lands; Cultivation; Deep learning; Effectiveness; High resolution; Image processing; Image resolution; Image segmentation; multi-scale segmentation; precise parcel extraction; Precision farming; Remote monitoring; Remote sensing; Segment Anything Model (SAM); Semantic segmentation; Semantics
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16183489

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.