基于面向对象法与U-Net模型的广东省云浮市云城区耕地后备资源遥感提取

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中图分类号：TP75；P208 文献标志码：A 文章编号：1001-411X（2026）01-0042-10

Abstract： 【Objective】To improve the eficiency and precision of cultivated land reserve resource information extraction，met the demands of modern agricultural development for land resource dynamic monitoring. 【Method】 This paper took Yuncheng District in Yunfu City of Guangdong Province as the study area， and proposed a method for extracting cultivated land reserve resources by integrating object-oriented rule construction and deep learning. Using GF-6 high-resolution satelite imagery， multi-scale image segmentation was performed，and a stepwise elimination method was applied to construct land classification identification rules， extracting samples of typical land types. Subsequently， based on the rule-based samples， a training label dataset for the U-Net deep learning model was constructed to accomplish the extractionand classification of cultivated land reserve resources. 【Result】For Yuncheng District， the optimal segmentation scale was determined to be 300. At this scale，features of the same category were efectively segmented， with clear boundaries between grassland and bare land.The overallprecision ofthe proposed method in the study area reached 87.3% ， while the mean intersection over union and F1 score achieved 75.4% and 86.7% ，respectively， enabling precise extraction of complex feature boundaries. The deep learning approach based on the improved U-Netefectivelyreduced misclasification，particularlyinareas with blurred boundariesand mixed pixels，and improved precision by approximately 5 percentage points compared to traditional object-oriented method. 【Conclusion】The remote sensing intelligent extraction method developed in this study demonstrates both high precision and time efficiency.It can provide robust support for local land use planning，cultivated land resource management， and ecological conservation， showing promising potential for broader application.

Key words： Remote sensing; Cultivated land reserve resource; Object-oriented; Multi-scale segmentation; Rule set： Deep learning

耕地后备资源特指在现有技术、经济条件下可开发为耕地的非耕地地类，包括未利用地中的可开发部分（农用地和建设用地以外的土地）、其他农用地中的潜在耕地（林地、其他草地等）、建设用地中的可复垦土地（某些建设用地，如废弃的工厂、矿场等），可为耕地合理开发利用提供重要支撑[1-4]。（剩余8945字）