The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks
文献类型：期刊论文
| 作者 | Song, Jia2,3; Yan, Xiangbing1,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023 |
| 卷号 | 15期号:2页码:17 |
| 关键词 | water body extraction deep learning negative sample cloud shadow interference semantic segmentation |
| DOI | 10.3390/rs15020514 |
| 通讯作者 | Song, Jia(songj@igsnrr.ac.cn) |
| 英文摘要 | Water resources are important strategic resources related to human survival and development. Water body extraction from remote sensing images is a very important research topic for the monitoring of global and regional surface water changes. Deep learning networks are one of the most effective approaches and training data is indispensable for ensuring the network accurately extracts water bodies. The training data for water body extraction includes water body samples and non-water negative samples. Cloud shadows are essential negative samples due to the high similarity between water bodies and cloud shadows, but few studies quantitatively evaluate the impact of cloud shadow samples on the accuracy of water body extraction. Therefore, the training datasets with different proportions of cloud shadows were produced, and each of them includes two types of cloud shadow samples: the manually-labeled cloud shadows and unlabeled cloud shadows. The training datasets are applied on a novel transformer-based water body extraction network to investigate how the negative samples affect the accuracy of the water body extraction network. The evaluation results of Overall Accuracy (OA) of 0.9973, mean Intersection over Union (mIoU) of 0.9753, and Kappa of 0.9747 were obtained, and it was found that when the training dataset contains a certain proportion of cloud shadows, the trained network can handle the misclassification of cloud shadows well and more accurately extract water bodies. |
| WOS关键词 | TIBETAN PLATEAU ; CLOUD SHADOW ; SAR IMAGES |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000915898500001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/189490]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Song, Jia |
| 作者单位 | 1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430072, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, 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 | Song, Jia,Yan, Xiangbing. The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks[J]. REMOTE SENSING,2023,15(2):17. |
| APA | Song, Jia,&Yan, Xiangbing.(2023).The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks.REMOTE SENSING,15(2),17. |
| MLA | Song, Jia,et al."The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks".REMOTE SENSING 15.2(2023):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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