The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks
(This article belongs to the Section AI Remote Sensing)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Water Body Extraction Network
2.4. Preparation for Training Data
3. Experiment and Results
3.1. Experiment Settings
3.2. Metrics of Assessment
3.3. Results
3.3.1. Accuracy Evaluation
3.3.2. Prediction Results
- (1)
- Cloud shadows being misclassified as water bodies
- (2)
- Precision of water boundaries
- (3)
- Water bodies being missed during extraction
- (4)
- The middle part of islands being misclassified as water body
- (5)
- Cloud shadow extraction results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Training Data Group | Training Dataset | Water Body | Cloud Shadow | ||
---|---|---|---|---|---|
Proportion | Labeled | Proportion | Labeled | ||
Water group | Water_P1 | 11% | Yes | 1% | No |
Water_P3 | 11% | Yes | 3% | No | |
Water_Shadow group | Water_Shadow_P1 | 11% | Yes | 1% | Yes |
Water_Shadow_P3 | 11% | Yes | 3% | Yes |
Training Dataset | OA | mIoU | Kappa | Epoch | |
---|---|---|---|---|---|
Top 1 | Water _P1 | 0.9970 | 0.9734 | 0.9728 | 180 |
Water _P3 | 0.9976 | 0.9778 | 0.9774 | 138 | |
Water_Shadow _P1 | 0.9891 | 0.9185 | 0.9459 | 61 | |
Water_Shadow _P3 | 0.9917 | 0.9429 | 0.9589 | 69 | |
Top 2 | Water _P1 | 0.9962 | 0.9664 | 0.9654 | 199 |
Water _P3 | 0.9974 | 0.9766 | 0.9761 | 191 | |
Water_Shadow _P1 | 0.9886 | 0.9081 | 0.9429 | 60 | |
Water_Shadow _P3 | 0.9899 | 0.9317 | 0.9507 | 92 | |
Top 3 | Water _P1 | 0.9940 | 0.9488 | 0.9464 | 211 |
Water _P3 | 0.9968 | 0.9714 | 0.9707 | 135 | |
Water_Shadow _P1 | 0.9876 | 0.9035 | 0.9383 | 90 | |
Water_Shadow _P3 | 0.9892 | 0.9252 | 0.9472 | 70 | |
Average | Water _P1 | 0.9957 | 0.9629 | 0.9615 | - |
Water _P3 | 0.9973 | 0.9753 | 0.9747 | - | |
Water_Shadow _P1 | 0.9884 | 0.91 | 0.9424 | - | |
Water_Shadow _P3 | 0.9903 | 0.9333 | 0.9523 | - |
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Song, J.; Yan, X. The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks. Remote Sens. 2023, 15, 514. https://doi.org/10.3390/rs15020514
Song J, Yan X. The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks. Remote Sensing. 2023; 15(2):514. https://doi.org/10.3390/rs15020514
Chicago/Turabian StyleSong, Jia, and **angbing Yan. 2023. "The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks" Remote Sensing 15, no. 2: 514. https://doi.org/10.3390/rs15020514