Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM

Yong Dong, Hongyan Wang (), Yuan Zhang, Xin Du, Qiangzi Li (), Yueting Wang, Yunqi Shen, Sichen Zhang, Jing Xiao, Jingyuan Xu, Sifeng Yan, Shuguang Gong and Haoxuan Hu
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Yong Dong: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Hongyan Wang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Yuan Zhang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Xin Du: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Qiangzi Li: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Yueting Wang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Yunqi Shen: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Sichen Zhang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Jing Xiao: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Jingyuan Xu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Sifeng Yan: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Shuguang Gong: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Haoxuan Hu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

Agriculture, 2025, vol. 15, issue 9, 1-20

Abstract: Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining multi-resolution remote sensing images based on the Segment Anything Model (SAM). Using cropland masking, overlap prediction and post-processing, we achieved 10 m-resolution parcel extraction with SAM, with performance in plain areas comparable to existing deep learning models (P: 0.89, R: 0.91, F1: 0.91, IoU: 0.87). Notably, in hilly regions with fragmented cultivated land, our approach even outperformed these models (P: 0.88, R: 0.76, F1: 0.81, IoU: 0.69). Subsequently, the 10 m parcels results were utilized to crop the high-resolution image. Based on the histogram features and internal edge features of the parcels, used to determine whether to segment downward or not, and at the same time, by setting the adaptive parameters of SAM, sub-meter parcel extraction was finally realized. Farmland boundaries extracted from high-resolution images can more accurately characterize the actual parcels, which is meaningful for farmland production and management. This study extended the application of deep learning large models in remote sensing, and provided a simple and fast method for accurate extraction of parcels boundaries.

Keywords: agriculture; remote sensing; Segment Anything Model; cropland; parcel extraction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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