A lightweight and scalable greenhouse mapping method based on remote sensing imagery
文献类型：期刊论文
| 作者 | Chen, Wei1; Wang, Qingpeng1; Wang, Dongliang2; Xu, Yameng1; He, Yingxuan1; Yang, Lan1; Tang, Hongzhao3 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 125页码:13 |
| 关键词 | Greenhouses Deep learning Semantic segmentation Remote sensing imagery Low-cost |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2023.103553 |
| 通讯作者 | Wang, Dongliang(wangdongliang@igsnrr.ac.cn) |
| 英文摘要 | Seeking a low-cost, high-efficiency greenhouse mapping technology has immense significance. While greenhouse extraction methods using deep learning have been proposed, the challenge of extracting dense small objects remains an unresolved problem. The inherent downscaling strategy in general-purpose semantic segmentation (SS) models renders them unsuitable for such tasks. In contrast, the dramatically increasing computational complexity associated with this problem may result in an unaffordable cost for consumer-level applications. To address the aforementioned challenges, this study presents a novel greenhouse mapping model based on remote sensing (RS) images, which not only exhibits high precision and robust generalization capabilities but also offers significant lightweight advantages. To meet broader needs, we also provide corresponding customizable and scalable rules that allow for a trade-off between accuracy and speed. To evaluate the performance of our model, we select several representative works to conduct benchmark experiments on a self-annotated dataset. The results demonstrate that our method can provide more powerful visual representations for greenhouse segmentation with minimal cost. Compared to the control group, the proposed method achieves an mIoU improvement of 1.116 %-10.77 % using only 3.282 M parameters, while maintaining a considerable inference speed. Code will be available at: https://github.com/W-qp/EGENet.git |
| WOS关键词 | OBJECT-BASED CLASSIFICATION ; SEMANTIC SEGMENTATION ; SENTINEL-2 MSI |
| 资助项目 | Strategic Priority Research Program of Chinese Academy of Sciences[XDA26010201] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001111990400001 |
| 出版者 | ELSEVIER |
| 资助机构 | Strategic Priority Research Program of Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200588]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Dongliang |
| 作者单位 | 1.China Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Minist Nat Resources, Land Satellite Remote Sensing Applicat Ctr, Beijing 100048, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Wei,Wang, Qingpeng,Wang, Dongliang,et al. A lightweight and scalable greenhouse mapping method based on remote sensing imagery[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2023,125:13. |
| APA | Chen, Wei.,Wang, Qingpeng.,Wang, Dongliang.,Xu, Yameng.,He, Yingxuan.,...&Tang, Hongzhao.(2023).A lightweight and scalable greenhouse mapping method based on remote sensing imagery.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,125,13. |
| MLA | Chen, Wei,et al."A lightweight and scalable greenhouse mapping method based on remote sensing imagery".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 125(2023):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。