Nonagriculturalization Detection Based on Vector Polygons and Contrastive Learning with High-Resolution Remote Sensing Images

被引：0

作者：

Zhang, Hui ^{[1
]}

Liu, Wei ^{[1
]}

Zhu, Changming ^{[1
]}

Niu, Hao ^{[1
]}

Yin, Pengcheng ^{[2
]}

Dong, Shiling ^{[2
]}

Wu, Jialin ^{[1
]}

Li, Erzhu ^{[1
]}

Zhang, Lianpeng ^{[1
]}

机构：

[1] Jiangsu Normal University, School of Geography, Geomatics, and Planning, Xuzhou,221116, China

[2] Bureau of Natural Resources and Planning of Xuzhou, Xuzhou,221006, China

来源：

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 2024年 / 17卷

关键词：

The conversion of agricultural lands; termed; 'nonagriculturalization; ' poses profound threats to food security and ecological stability. Remote sensing image change detection offers an invaluable tool for monitoring this phenomenon. However; most change detection techniques prioritize image comparison over exploiting accumulated vector datasets. Additionally; many current methods are not readily applicable in practical scenarios due to inadequate model generalization capabilities and a scarcity of samples; resulting in a continued reliance on manual intervention for nonagriculturalization detection. In response; this article introduces a novel change detection approach for nonagriculturalization based on the vector data and contrastive learning. Initially; the boundary-constrained simple noniterative clustering algorithm is applied to segment two-phase images under vector data guidance. Samples are then generated using an adaptive cropping method. For early phase image samples; a collaborative validation-based sample annotation framework is employed to optimize and annotate the samples; with the purified high-quality samples serving as the training set for subsequent classification. For later-phase image samples; only those within the cropland vector polygons are retained for prediction. Building on this; a semi-supervised cross-domain contrastive learning framework is proposed for remote sensing scene classification. Ultimately; by integrating nonagriculturalization rules and postprocessing techniques; areas undergoing nonagriculturalization are further detected. Validating our methodology on Wuxi and Yangzhou datasets yielded precision rates of 91.57% and 89.21%; with recall rates of 93.68% and 90.51%; respectively. These outcomes affirm the effectiveness of our method in nonagriculturalization detection; offering robust technical support for research in this domain. © 2008-2012 IEEE;

D O I：

10.1109/JSTARS.2024.3476131

中图分类号：

学科分类号：

摘要：

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页码：18474 / 18488

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