Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data
Abstract
:1. Introduction
2. Experimental Data Collection
2.1. The Study Areas and Sentinel-2 Imagery
2.2. In-Situ Data
3. Proposed Machine Learning Algorithms for Bathymetry Mapping
3.1. Stumpf Model
3.2. Log-Linear Model
3.3. Baidu Easy DL Model
- (a)
- Data preparation: Upload or import the data table that is to be used for prediction. Easy-DL supports multiple data formats, such as CSV, Excel, JSON, etc.
- (b)
- Model selection: select a suitable pre-trained model for prediction based on the characteristics of the table data and the prediction requirements.
- (c)
- Data preprocessing: preprocess the table data, including data cleaning, feature selection, data enhancement, and table format conversion, to improve the training effect of the model.
- (d)
- Model training: Based on the uploaded table data and the selected model, Easy-DL automatically performs model training. During the training process, you can monitor the training progress and view performance indicators during training.
- (e)
- Model evaluation: after the model training is complete, Easy-DL provides a series of evaluation indicators, such as accuracy, precision, and recall, to evaluate the performance of the model.
- (f)
- Model deployment: After model evaluation is complete, the trained model can be deployed to the production environment. Easy-DL provides multiple deployment options, such as API, SDK, and a custom code, to meet different application needs.
- (g)
- Prediction: The deployed model can be applied to actual scenarios for prediction. Through the API or SDK provided by Easy-DL, the prediction of the table can be easily performed.
3.4. Accuracy Evaluation Methods
3.5. Bathymetry Mapping
4. Experimental Setup and Results
5. Discussion
5.1. The Performance of Water Depth Inversion Model
5.2. The Uncertainty and Implications of Baidu Easy-DL Model
- (a)
- Model Selection: The choice of the model may affect the accuracy of the inversion results. Although machine learning models generally have higher robustness than traditional semi-empirical, bio-optical, and semi-analytical models², different machine learning models may produce different results. For example, a study found that the Genetic Algorithm Optimized Extreme Learning Machine (GA-ELM) had a more compact network structure and better generalization ability than the Extreme Learning Machine (ELM).
- (b)
- Input Variables: The choice of input variables may also affect the results. For example, using remote sensing reflectance values at different bands as input variables may lead to different inversion results.
- (c)
- Data Quality: The quality of remote sensing data also affects the inversion results. For example, if remote sensing data contain noise or are affected by factors such as atmosphere and water turbidity, it may lead to inaccurate inversion results.
- (a)
- Depth Inversion: This work is of paramount importance for depth inversion, offering valuable support for marine engineering, shipping, and maritime military security.
- (b)
- Environmental Monitoring: This methodology can also be utilized for environmental monitoring, such as monitoring the water quality of inland bodies of water.
- (c)
- Scientific Advancement: This work can propel scientific progress in related fields, such as enhancing the accuracy and robustness of remote sensing inversion models.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Training Method | RMSE | ||||
---|---|---|---|---|---|
0–3 m (580 Points) | 3–6 m (214 Points) | 6–9 m (200 Points) | >9 m (95 Points) | Overall (1089 Points) | |
Stumpf | 1.12 | 1.01 | 0.94 | 1.19 | 1.08 |
Log-Linear | 0.59 | 0.58 | 0.66 | 0.89 | 0.63 |
Easy-DL | 0.43 | 0.29 | 0.23 | 0.39 | 0.39 |
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Wu, Z.; Wu, S.; Yang, H.; Mao, Z.; Shen, W. Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data. Remote Sens. 2023, 15, 4955. https://doi.org/10.3390/rs15204955
Wu Z, Wu S, Yang H, Mao Z, Shen W. Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data. Remote Sensing. 2023; 15(20):4955. https://doi.org/10.3390/rs15204955
Chicago/Turabian StyleWu, Zhongqiang, Shulei Wu, Haixia Yang, Zhihua Mao, and Wei Shen. 2023. "Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data" Remote Sensing 15, no. 20: 4955. https://doi.org/10.3390/rs15204955
APA StyleWu, Z., Wu, S., Yang, H., Mao, Z., & Shen, W. (2023). Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data. Remote Sensing, 15(20), 4955. https://doi.org/10.3390/rs15204955