Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia
文献类型：期刊论文
| 作者 | Li, Kai2,3; Wang, Juanle3 ; Yao, Jinyi1,3
|
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2021-12-01 |
| 卷号 | 103页码:11 |
| ISSN号 | 1569-8432 |
| 关键词 | Water Segmentation Pixel-based CNN ROI U-net Mongolia |
| DOI | 10.1016/j.jag.2021.102497 |
| 通讯作者 | Wang, Juanle(wangjl@igsnrr.ac.cn) |
| 英文摘要 | The carrying capacity of water resources is key to the sustainable development of arid and semi-arid regions. There are important challenges related to the detection of discontinuous and crooked water bodies in the vast Mongolian Plateau, despite the availability of remote sensing technology which has the advantage of facilitating water observations over large areas and timelines. Given the high cost and low coverage of high-resolution images and the low resolution of images with high coverage, this study proposes a pixel-based convolutional neural network (CNN) method for the application of water extracted from the region of interest (ROI) to mediumresolution Landsat images. The pixel-based CNN method combines the texture and spectral features of the ground object by connecting the center pixels of the images to the surrounding pixels. ROI is used instead of full-label datasets, reduce the difficulty of building labels in low-to-medium-resolution images. Taking the Tuul River in Mongolia as a case, the pixel-based CNN method, the normalized difference water index threshold (NDWI) method, the modified normalized difference water index (MNDWI) threshold method, U-net model in deep learning, and the pixel-based deep neural network (DNN) method were used with medium-resolution Landsat 8 images with ROI labels. The pixel-based CNN method shows better water extraction results for the cloud, cloud shadows, and building areas, compared with other methods. The method proposed in this study had the highest verification accuracy (92.07%). It also has the advantages of fewer training parameters and shorter training time. The training accuracies of the pixel-based CNN, pixel-based DNN, and U-net were 99.90%, 96.98%, and 93.70%, respectively. All training models and calling methods were uploaded to GitHub (https://github.com/CaryLee 17/Pixel-based-CNN). |
| WOS关键词 | SPECTRAL CHARACTERISTICS ; SATELLITE IMAGERY ; INDEX NDWI ; CLASSIFICATION ; BODY |
| 资助项目 | National Natural Science Foundation of China[41971385] ; Strategic Priority Research Program (Class A) of the Chinese Academy of Sciences[XDA2003020302] ; Construction Project of the China Knowledge Center for Engineering Sciences and Technology[CKCEST-2021-2-18] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000696914600002 |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program (Class A) of the Chinese Academy of Sciences ; Construction Project of the China Knowledge Center for Engineering Sciences and Technology |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/166033]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Juanle |
| 作者单位 | 1.Shandong Univ Technol, Sch Civil & Architectural Engn, Zibo 255049, Peoples R China 2.China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Kai,Wang, Juanle,Yao, Jinyi. Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2021,103:11. |
| APA | Li, Kai,Wang, Juanle,&Yao, Jinyi.(2021).Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,103,11. |
| MLA | Li, Kai,et al."Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 103(2021):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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