Large-Scale Individual Plastic Greenhouse Extraction Using Deep Learning and High-Resolution Remote Sensing Imagery
文献类型：期刊论文
| 作者 | Chang, Yuguang1; Yu, Xiaoyu2,3; Li, Baipeng3; Tian, Xiangyu3,4; Wu, Zhaoming3 |
| 刊名 | AGRONOMY-BASEL
 |
| 出版日期 | 2025-08-21 |
| 卷号 | 15期号:8页码:2014 |
| 关键词 | agricultural monitoring individual plastic greenhouses GF-2 remote sensing imagery feature fusion semantic segmentation |
| DOI | 10.3390/agronomy15082014 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | Addressing the demands of agricultural resource digitization and facility crop monitoring, precise extraction of plastic greenhouses using high-resolution remote sensing imagery demonstrates pivotal significance for implementing refined farmland management. However, the complex spatial topological relationships among densely arranged greenhouses and the spectral confusion of ground objects within agricultural backgrounds limit the effectiveness of conventional methods in the large-scale, precise extraction of plastic greenhouses. This study constructs an Individual Plastic Greenhouse Extraction Network (IPGENet) by integrating a multi-scale feature fusion decoder with the Swin-UNet architecture to improve the accuracy of large-scale individual plastic greenhouse extraction. To ensure sample accuracy while reducing manual labor costs, an iterative sampling approach is proposed to rapidly expand a small sample set into a large-scale dataset. Using GF-2 satellite imagery data in Shandong Province, China, the model realized large-scale mapping of individual plastic greenhouse extraction results. In addition to large-scale sub-meter extraction and mapping, the study conducted quantitative and spatial statistical analyses of extraction results across cities in Shandong Province, revealing regional disparities in plastic greenhouse development and providing a novel technical approach for large-scale plastic greenhouse mapping. |
| URL标识 | 查看原文 |
| WOS研究方向 | Agriculture ; Plant Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001557221400001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216037]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Chang, Yuguang |
| 作者单位 | 1.Henan Polytech Univ, Sch Civil Engn, Jiaozuo 454000, Peoples R China; 2.Henan Polytech Univ, Sch Resources & Environm, Jiaozuo 454000, Peoples R China; 3.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing & Digital Earth, Beijing 100101, Peoples R China; 4.Chinese Acad Sci, Key Lab Ecosyst Network Observat & Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chang, Yuguang,Yu, Xiaoyu,Li, Baipeng,et al. Large-Scale Individual Plastic Greenhouse Extraction Using Deep Learning and High-Resolution Remote Sensing Imagery[J]. AGRONOMY-BASEL,2025,15(8):2014. |
| APA | Chang, Yuguang,Yu, Xiaoyu,Li, Baipeng,Tian, Xiangyu,&Wu, Zhaoming.(2025).Large-Scale Individual Plastic Greenhouse Extraction Using Deep Learning and High-Resolution Remote Sensing Imagery.AGRONOMY-BASEL,15(8),2014. |
| MLA | Chang, Yuguang,et al."Large-Scale Individual Plastic Greenhouse Extraction Using Deep Learning and High-Resolution Remote Sensing Imagery".AGRONOMY-BASEL 15.8(2025):2014. |
入库方式： OAI收割
来源：地理科学与资源研究所
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