Research progress in farmland boundary extraction methods based on remote sensing image

doi:10.13733/j.jcam.issn.20955553.2024.12.025

Abstract

Abstract:

The automated and accurate extraction of farmland boundaries plays a crucial role in effectively quantifying agricultural land resources and setting relevant regulatory policies. It is essential to the development of modern agriculture and smart agriculture. This paper compiles commonly used remote sensing data across various extraction scales and outlining the development of boundary extraction techniques from unsupervised to supervised methods. The edge-based, region-based, hybrid and deep learning methods were comprehensively summarized and compared. Deep learning, a supervised extraction technique, is closely linked to advancements in remote sensing technology. Accuracy assessment involves reference image acquisition and evaluation methods. Current challenges include the underutilization and limited exploration of farmland boundary features, the lack of publicly available and real-time datasets, the scarcity of research focused on smallholder areas, and the limited applicability of existing extraction methods. In order to overcome these challenges, the future directions of research were proposed, such as the integrated utilization of different farmland boundary features, the establishment of data-sharing platforms for comprehensive farmland boundary data, the application of cloud computing in farmland boundary analysis, the development of specialized extraction methods for smallholder areas, and the integration of spatiotemporal data from multiple sources.

Key words: farmland boundary, land segmentation, edge detection, image processing

摘要：

农田边界的自动提取对高效量化农田土地资源和制定相关调控政策十分重要，是农业现代化和智慧化发展的必要条件。统计多种农田边界提取尺度下常用的遥感数据，简述农田边界提取技术经历了从无监督到监督提取的发展，总结并对比农田边界提取的4种主要方法，包括基于边缘检测的方法、基于区域的方法、混合方法和深度学习方法，深度学习方法属于监督提取技术，与遥感技术的发展息息相关。简述农田边界提取精度评定中的参考图像获取与精度评估方法。提出目前存在农田边界特征利用和挖掘不足、缺乏公开实时的数据集、缺乏针对小农地区的研究、提取方法普适性不足的问题。在综合应用农田边界特征、共享农田边界大数据和应用云计算平台、开发小农地区农田边界提取方法、融合时空协同的多源数据方面作出展望。

关键词: 农田边界, 地块分割, 边缘检测, 图像处理

CLC Number:

S127

Kong Mingfeng, , Zheng Haifeng, . Research progress in farmland boundary extraction methods based on remote sensing image[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(12): 168-174.

孔鸣凤, 郑海峰, . 基于遥感影像的农田边界提取方法研究进展[J]. 中国农机化学报, 2024, 45(12): 168-174.

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