A Survey of Farmland Boundary Extraction Technology Based on Remote Sensing Images

Farmland boundary information plays a key role in agricultural remote sensing, and it is of importance to modern agriculture. We collected the relevant research in this field at home and abroad in this review, and we systematically assessed the farmland boundary extraction process, detection algorithms, and influencing factors. In this paper, we first discuss the five parts of the assessment: (1) image acquisition; (2) preprocessing; (3) detection algorithms; (4) postprocessing; (5) the evaluation of the boundary information extraction process. Second, we discuss recognition algorithms. Third, we discuss various detection algorithms. The detection algorithms can be divided into four types: (1) low-level feature extraction algorithms, which only consider the boundary features; (2) high-level feature extraction algorithms, which consider boundary information and other image information simultaneously; (3) visual hierarchy extraction algorithms, which simulate biological vision systems; (4) boundary object extraction algorithms, which recognize boundary object extraction ideas. We can subdivide each type of algorithm into several algorithm subclasses. Fourth, we discuss the technical factors and natural factors that affect boundary extraction. Finally, we summarize the development history of this field, and we analyze the problems that exist, such as the lack of algorithms that can be adapted to higher-resolution images, the lack of algorithms with good practical ability, and the lack of a unified and effective evaluation index system.

Automated summary

This review collected and assessed the research on farmland boundary information extraction, detection algorithms, and influencing factors. It discussed recognition and detection algorithms, technical and natural factors affecting boundary extraction. The development history and existing problems were summarized.