Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Methods
2.3.1. The Normalized Difference Water Index (NDWI)
2.3.2. Evolution of Convolutional Neural Network
2.3.3. Model-Based on DenseNet
3. Results
3.1. The Image Preprocessing
3.2. Water Identification Result of DenseNet
3.3. Working Efficiency of DenseNet, ResNet, VGG, SegNet and DeepLab v3+ Models
3.4. Comparison of Identification Results
3.5. Interannual Variations of the Water Areas
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Network | Time | P | R | F1 | mIoU |
---|---|---|---|---|---|
DenseNet63 | 15,463 s | 0.959 | 0.900 | 0.928 | 0.867 |
DenseNet79 | 16,377 s | 0.961 | 0.904 | 0.931 | 0.872 |
DenseNet121 | 20,611 s | 0.957 | 0.901 | 0.928 | 0.866 |
DenseNet169 | 24,018 s | 0.964 | 0.896 | 0.928 | 0.867 |
DenseNet201 | 27,121 s | 0.960 | 0.899 | 0.929 | 0.867 |
Network | Time |
---|---|
DenseNet | 16,377 s |
ResNet | 19,436 s |
VGG | 21,471 s |
SegNet | 19,021 s |
DeepLab v3+ | 11,924 s |
DenseNet | ResNet | VGG | SegNet | DeepLab v3+ | NDWI | |
---|---|---|---|---|---|---|
P | 0.961 ± 0.011 | 0.936 ± 0.014 | 0.914 ± 0.016 | 0.911 ± 0.017 | 0.922 ± 0.016 | 0.702 ± 0.027 |
R | 0.904 ± 0.017 | 0.902 ± 0.017 | 0.915 ± 0.016 | 0.934 ± 0.015 | 0.917 ± 0.016 | 0.983 ± 0.007 |
F1 | 0.931 ± 0.015 | 0.919 ± 0.016 | 0.914 ± 0.016 | 0.922 ± 0.016 | 0.919 ± 0.016 | 0.819 ± 0.023 |
mIoU | 0.872 ± 0.020 | 0.850 ± 0.021 | 0.842 ± 0.021 | 0.856 ± 0.021 | 0.850 ± 0.021 | 0.767 ± 0.025 |
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Wang, G.; Wu, M.; Wei, X.; Song, H. Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks. Remote Sens. 2020, 12, 795. https://doi.org/10.3390/rs12050795
Wang G, Wu M, Wei X, Song H. Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks. Remote Sensing. 2020; 12(5):795. https://doi.org/10.3390/rs12050795
Chicago/Turabian StyleWang, Guojie, Mengjuan Wu, Xikun Wei, and Huihui Song. 2020. "Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks" Remote Sensing 12, no. 5: 795. https://doi.org/10.3390/rs12050795