Integration of Photogrammetric and Spectral Techniques for Advanced Drone-Based Bathymetry Retrieval Using a Deep Learning Approach
Abstract
:1. Introduction
1.1. Optical Remote Sensing in Seafloor Mapping
1.2. Satellite-Derived Bathymetry
1.3. Structure from Motion
1.4. Aim of the Study
2. Methodology
2.1. Study Areas
2.2. Onshore Survey and Drone Platform Configuration
2.3. USV Surveys
2.4. Structure from Motion
2.5. Data Pre-Processing
2.6. Convolutional Network Architecture and Training Set
3. Results
3.1. Bathymetry Results on the Study Areas
3.2. Ablation Study
3.3. Sensitivity Analysis of the Train–Test Split
3.4. Comparison with Artificial Neural Networks and Conventional Machine Learning Methods
3.5. Cross-Validation Study
4. Discussion
4.1. Algorithm Performance
4.2. Future Considerations
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Area | Date(dd/mm/yyyy), Local Time (HH:MM) | Flight Altitude (M) | Number of Images |
---|---|---|---|
Stavros | 23/12/2020, 12:00 | 52 | 420 |
Kalamaki | 29/03/2021, 11:30 | 120 | 734 |
Elafonisi | 12/04/2021, 12:30 | 120 | 1350 |
Single Stack Hourglass Model | ||
---|---|---|
Rasters Used | RMSE | R2 |
RGB | 0.66 m | 62.2% |
RGB + SfM | 0.62 m | 67.7% |
RGB + DistCoast | 0.51 m | 74.6% |
RGB + SfM + DistCoast | 0.43 m | 85.4% |
Triple Stack Hourglass Model | ||
RGB | 0.54 m | 68.5% |
RGB + SfM | 0.52 m | 68.7% |
RGB + DistCoast | 0.48 m | 75.8% |
RGB + SfM + DistCoast | 0.41 m | 85.7% |
Full Stack Hourglass Model | ||
RGB | 0.49 m | 79.5% |
RGB + SfM | 0.48 m | 81.4% |
RGB + DistCoast | 0.42 m | 83.8% |
RGB + SfM + DistCoast | 0.35 m | 89.4% |
Training/Test Ratio | ||||||
---|---|---|---|---|---|---|
70%/30% | 60%/40% | 50%/50% | 40%/60% | 30%/70% | ||
Stavros | RMSE | 0.079 m | 0.088 m | 0.098 m | 0.179 m | 0.236 m |
R2 | 99.3% | 99.0% | 97.7% | 96.2% | 94.1% | |
Kalamaki | RMSE | 0.301 m | 0.346 m | 0.362 m | 0.423 m | 0.612 m |
R2 | 91.9% | 89.4% | 87.4% | 84.3% | 79.7% | |
Elafonisi | RMSE | 0.315 m | 0.327 m | 0.382 m | 0.604 m | 0.876 m |
R2 | 85.5% | 84.5% | 79.5% | 54.0% | 45.4% |
Our Pipeline with CNN (Full Model) | Our Pipeline with RF | Our Pipeline with SVM | |
---|---|---|---|
RMSE | 0.346 m | 0.432 m | 0.599 m |
R2 | 89.4% | 84.1% | 67.5% |
Trained on Stavros | Trained on Kalamaki | Trained on Elafonisi | |
---|---|---|---|
Tested on Stavros | 0.043 m | 0.753 m | 0.698 m |
Tested on Kalamaki | 1.754 m | 0.248 m | 1.058 m |
Tested on Elafonisi | 0.630 m | 0.773 m | 0.138 m |
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Alevizos, E.; Nicodemou, V.C.; Makris, A.; Oikonomidis, I.; Roussos, A.; Alexakis, D.D. Integration of Photogrammetric and Spectral Techniques for Advanced Drone-Based Bathymetry Retrieval Using a Deep Learning Approach. Remote Sens. 2022, 14, 4160. https://doi.org/10.3390/rs14174160
Alevizos E, Nicodemou VC, Makris A, Oikonomidis I, Roussos A, Alexakis DD. Integration of Photogrammetric and Spectral Techniques for Advanced Drone-Based Bathymetry Retrieval Using a Deep Learning Approach. Remote Sensing. 2022; 14(17):4160. https://doi.org/10.3390/rs14174160
Chicago/Turabian StyleAlevizos, Evangelos, Vassilis C. Nicodemou, Alexandros Makris, Iason Oikonomidis, Anastasios Roussos, and Dimitrios D. Alexakis. 2022. "Integration of Photogrammetric and Spectral Techniques for Advanced Drone-Based Bathymetry Retrieval Using a Deep Learning Approach" Remote Sensing 14, no. 17: 4160. https://doi.org/10.3390/rs14174160