.png){kind=icon}
返回
A study on automatic annotation methods for watershed environmental elements based on semantic segmentation models
.png){kind=icon}
OA
AI
DOI：10.1080/22797254.2025.2473939.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Satellite remote sensing data provide a crucial spatiotemporal foundation for advancing digital-twin water conservancy construction in China. Given the complex and variable nature of watersheds and their surrounding elements, using remote sensing imagery to intelligently interpret watershed elements poses significant challenges. Deep neural network models offer a potential unified solution; however, their effectiveness hinges on the quality and diversity of the training data. In a data-driven analytical approach, this study explores the segmentation accuracy and efficiency of intelligent-interpretation neural networks from the perspectives of watershed monitoring element types and sample annotation methods. Hence, experiments are conducted in three modules: (1) Determination of watershed environmental element sample types. (2) Semi-automated annotated sample production method. This study utilizes the Random Forest classifier (RF) and Segment Anything Model (SAM) to create label sets, replacing open-source land cover datasets to achieve superior watershed-environment segmentation results. (3) Comparison of different neural network segmentation capabilities. In the study area, SegFormer combined with SAM labels achieved an overall accuracy (OA) of 96.71%. The OA of SegFormer combined with RF labels was 91.71%, representing improvements of 6.24% and 1.24%, respectively, over the Sentinel-2 Land Cover Explorer (OA of 90.47%).
Keyword:
Watershed segmentation
automatic annotation of label sets
deep learning
SAM
RF
期刊
IF:
3.7
论文数:
921
被引数:
2.2K
机构
引用论文
Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016
OAAI
OAAI分享收藏Small water body extraction method based on Sentinel-2 satellite multi-spectral remote sensing imageOAAI
OAAI分享收藏Water Body Extraction of the Weihe River Basin Based on MF-SegFormer Applied to Landsat8 OLI Data基于MF-SegFormer应用于Landsat8 OLI数据的渭河流域水体提取OAAI
OAAI分享收藏Performance Evaluation of Machine Learning in Wireless Connected Robotics Swarms.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容