Application of Artificial Neural Networks for Mangrove Map** Using Multi-Temporal and Multi-Source Remote Sensing Imagery
Abstract
:1. Introduction
2. Study Area and Data Sets
2.1. Study Area
2.2. Reference Samples
2.3. Satellite Images
3. Methodology
3.1. Satellite Images Preprocessing
3.2. ANN Models and Classification
3.3. Accuracy Assessment
4. Results
4.1. Number of Layers and Neurons
4.2. Activation Function
4.3. Learning Rate
4.4. Mangrove Ecosystem Maps
5. Discussion
5.1. General Remarks
5.2. Impact of Data Standardization
5.3. Impact of Limited Training Samples
5.4. Impact of Noise Labels
5.5. Contribution of Multi-Temporal and Multi-Source Images
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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ID | Class | Training Samples | Test Samples | Total | |||
---|---|---|---|---|---|---|---|
Polygon | Area (ha) | Polygon | Area (ha) | Polygon | Area (ha) | ||
1 | Mangrove | 50 | 17.29 | 73 | 24.76 | 123 | 42.05 |
2 | Tidal zone | 33 | 20.05 | 30 | 18.85 | 63 | 38.90 |
3 | Deep water | 27 | 16.09 | 26 | 20.23 | 53 | 36.32 |
4 | Shallow water | 28 | 15.18 | 26 | 15.69 | 54 | 30.87 |
5 | Mudflat | 67 | 18.58 | 59 | 21.09 | 126 | 39.67 |
6 | Urban | 28 | 16.00 | 29 | 19.88 | 57 | 35.88 |
7 | Barren | 139 | 25.37 | 136 | 24.45 | 275 | 49.82 |
8 | Vegetation | 37 | 15.61 | 36 | 15.15 | 73 | 30.76 |
Total | 409 | 144.17 | 415 | 160.1 | 824 | 304.27 |
ID | Class | ANN Models with Learning Algorithms | |||||
---|---|---|---|---|---|---|---|
Adam | LBFGS | SGD | |||||
PA | UA | PA | UA | PA | UA | ||
1 | Mangrove | 98.5 | 99.7 | 97.9 | 99.9 | 98.8 | 100 |
2 | Tidal zone | 90.4 | 97.5 | 95.5 | 94.3 | 95.0 | 94.0 |
3 | Deep water | 98.3 | 99.4 | 98.0 | 99.1 | 98.5 | 98.1 |
4 | Shallow water | 96.5 | 88.1 | 92.2 | 92.1 | 90.3 | 92.0 |
5 | Mudflat | 100 | 97.9 | 99.3 | 98.4 | 99.7 | 98.8 |
6 | Urban | 95.4 | 97.4 | 89.9 | 95.0 | 89.8 | 96.5 |
7 | Barren | 97.5 | 95.9 | 96.1 | 91.2 | 97.8 | 91.2 |
8 | Vegetation | 99.2 | 98.9 | 98.5 | 96.5 | 98.6 | 98.0 |
OA = 97.02 and KC = 0.97 | OA = 96.00 and KC = 0.95 | OA = 96.30 and KC = 0.96 |
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Ghorbanian, A.; Ahmadi, S.A.; Amani, M.; Mohammadzadeh, A.; Jamali, S. Application of Artificial Neural Networks for Mangrove Map** Using Multi-Temporal and Multi-Source Remote Sensing Imagery. Water 2022, 14, 244. https://doi.org/10.3390/w14020244
Ghorbanian A, Ahmadi SA, Amani M, Mohammadzadeh A, Jamali S. Application of Artificial Neural Networks for Mangrove Map** Using Multi-Temporal and Multi-Source Remote Sensing Imagery. Water. 2022; 14(2):244. https://doi.org/10.3390/w14020244
Chicago/Turabian StyleGhorbanian, Arsalan, Seyed Ali Ahmadi, Meisam Amani, Ali Mohammadzadeh, and Sadegh Jamali. 2022. "Application of Artificial Neural Networks for Mangrove Map** Using Multi-Temporal and Multi-Source Remote Sensing Imagery" Water 14, no. 2: 244. https://doi.org/10.3390/w14020244