Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China
Abstract
:1. Introduction
2. Data Resources
2.1. Ground Data
2.2. SAR Data
3. Methodology
3.1. D CNNs
3.2. LSTM RNNs
3.3. GRU RNNs
3.4. RF
3.5. Classifier Training
3.6. Incremental Classification
3.7. Accuracy Assessment
4. Results
4.1. Temporal Profiles of the Sentinel-1A Backscatter Coefficient
4.2. Overall Accuracy Metrics
4.3. Incremental Classification Accuracy of Each Crop
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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ID | 1 | 2 | 3 | 4 | 5 | Total |
---|---|---|---|---|---|---|
Type | Paddy | Sugarcane | Banana | Pineapple | Eucalyptus | |
Number | 179 | 215 | 53 | 44 | 339 | 830 |
Model | Hyper-Parameter Name | Description | Tested Hyper-Parameter Values | Optimal Hyper-Parameter Value |
---|---|---|---|---|
num_filter1 | Number of filters in the first 1D Conv layer | 10, 12, 14, 16, 18 | 16 | |
num_filter2 | Number of filters in the second 1D Conv layer | 6, 8, 10, 12, 14, 16 | 14 | |
num_filter3 | Number of filters in the third 1D Conv layer | 4, 6, 8, 10 | 8 | |
num_neu1 | Number of neurons in the first fully connected layer | 20, 30, 36, 38, 40 | 38 | |
max_interations | Maximum number of iterations | 10,000, 12,000, 15,000, 20,000 | 10,000 | |
1D CNNs | batch_size | Number of samples for every batch of training | 32, 64, 128 | 64 |
dropout | Dropout rate of a neuron in the first fully-con layer | 0.5, 1 | 1 | |
learning_rate | Learning rate | 0.00001, 0.00002, 0.00003, 0.00004, 0.00005, 0.0001 | 0.00002 | |
LSTM RNNs | num_layers | Number of hidden layers | 1, 2, 3, 4 | 3 |
hidden_size | Number of hidden neurons per layer | 50, 100, 150, 200 | 100 | |
learning_rate | Learning rate | 0.0005, 0.005 0.004, 0.006 | 0.005 | |
dropout | Dropout rate of a neuron in hidden layers | 0.5, 1 | 1 | |
max_grad_norm | Maximum gradient norm | 1, 2.5, 5, 10 | 5 | |
max_interations | Maximum number of iterations | 10,000, 15,000, 18,000, 20,000 | 15,000 | |
batch_size | Number of samples for every batch of training | 32, 64, 128 | 64 | |
GRU RNNs | num_layers | Number of hidden layers | 1, 2, 3, 4 | 2 |
hidden_size | Number of hidden neurons per layer | 50, 100, 150, 200 | 200 | |
learning_rate | Learning rate | 0.0005, 0.005 0.004, 0.006 | 0.005 | |
dropout | Dropout rate of a neuron in hidden layers | 0.5, 1 | 1 | |
max_grad_norm | Maximum gradient norm | 1, 2.5, 5, 10 | 5 | |
max_interations | Maximum number of iterations | 10,000, 15,000, 18,000, 20,000 | 20,000 | |
batch_size | Number of samples for every batch of training | 32, 64, 128 | 64 | |
RF | n_estimators | Number of trees | 100, 200, 300, 400, 500 | 400 |
Reference Data | |||
---|---|---|---|
Crop | Urban | ||
Classified Data | Crop | A | B |
Urban | C | D |
Classifier | Kappa-Max | OA-Max | The Date of Maximum | First Date of Kappa ≥ 0.900 |
---|---|---|---|---|
1D CNNs | 0.942 | 0.959 | 21 February 2018 | 30 September 2017 |
LSTM RNNs | 0.931 | 0.951 | 23 December 2017 | 6 September 2017 |
GRU RNNs | 0.934 | 0.954 | 11 December 2017 | 6 September 2017 |
RF | 0.937 | 0.954 | 9 February 2018 | 24 October 2017 |
Crop | 1D CNNs PA | LSTM PA | GRU PA | RF PA |
---|---|---|---|---|
Paddy | 0.988 | 0.961 | 0.977 | 0.977 |
Sugarcane | 0.936 | 0.930 | 0.919 | 0.932 |
Banana | 0.945 | 0.944 | 0.981 | 0.929 |
Pineapple | 0.907 | 0.905 | 0.889 | 0.905 |
Eucalyptus | 0.968 | 0.965 | 0.970 | 0.970 |
Crop | 1D CNNs UA | LSTM UA | GRU UA | RF UA |
---|---|---|---|---|
Paddy | 0.944 | 0.961 | 0.966 | 0.955 |
Sugarcane | 0.953 | 0.921 | 0.949 | 0.949 |
Banana | 0.981 | 0.962 | 0.962 | 0.981 |
Pineapple | 0.886 | 0.864 | 0.909 | 0.864 |
Eucalyptus | 0.976 | 0.973 | 0.956 | 0.968 |
Classifier | Paddy | Sugarcane | Banana | Pineapple | Eucalyptus |
---|---|---|---|---|---|
1D CNNs | 13 August 2017 (0.903) | 18 September 2017 (0.900) | 1 August 2017 (0.914) | 16 January 2018 (0.911) | 20 July 2017 (0.917) |
LSTM RNNs | 6 September 2017 (0.935) | 18 September 2017 (0.906) | 13 August 2017 (0.947) | 8 July 2017 (0.900) | |
GRU RNNs | 6 September 2017 (0.942) | 24 October 2017 (0.921) | 14 June 2017 (0.911) | 8 July 2017 (0.909) | |
RF | 1 August 2017 (0.910) | 13 August 2017 (0.908) | 8 July 2017 (0.956) | 14 June 2017 (0.902) |
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Zhao, H.; Chen, Z.; Jiang, H.; Jing, W.; Sun, L.; Feng, M. Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China. Remote Sens. 2019, 11, 2673. https://doi.org/10.3390/rs11222673
Zhao H, Chen Z, Jiang H, Jing W, Sun L, Feng M. Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China. Remote Sensing. 2019; 11(22):2673. https://doi.org/10.3390/rs11222673
Chicago/Turabian StyleZhao, Hongwei, Zhongxin Chen, Hao Jiang, Wenlong Jing, Liang Sun, and Min Feng. 2019. "Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China" Remote Sensing 11, no. 22: 2673. https://doi.org/10.3390/rs11222673
APA StyleZhao, H., Chen, Z., Jiang, H., Jing, W., Sun, L., & Feng, M. (2019). Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China. Remote Sensing, 11(22), 2673. https://doi.org/10.3390/rs11222673