Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field
Abstract
:1. Introduction
- The statistical analysis technology is used to analyze the segmentation results of RefineNet, and prior knowledge is applied to PCCRF modeling.
- Based on prior knowledge, we modified the fully connected conditional random field (FCCRF) to build the PCCRF. We refined the definition of pairwise potential energy, employing a linear model to connect the unary potential energy and pairwise potential energy. Compared to the equal weight connection model used in the FCCRF, the new fusion model used in the PCCRF can better reflect the different roles of information generated from a larger receptive field and information generated from a smaller receptive field.
- We only used pixel pairs associated with the selected pixels in the PCCRF, which can effectively reduce the amount of data required for computing models and improve the computational efficiency of the PCCRF.
- Benefiting from the ability to describe the spatial correlation between pixel categories of a CRF, RefineNet-PCCRF can not only improve the classification accuracy of edge pixels in the winter wheat planting area, but it also has high computing efficiency.
2. Study Area and Dataset
2.1. Study Area
2.2. Remote Sensing and Pre-Processing
2.3. Create Image–Label Pair Dataset
3. Methodology
3.1. Improved RefineNet Model
3.2. Statistical Analysis of the Initial Segmentation Results
3.3. The PCCRF Model
3.3.1. Description of the Modeling Scheme
3.3.2. Features Selection
3.3.3. Definition of the Pairwise Potential Energy
3.3.4. Definition of PCCRF
3.4. PCCRF Training
- Pretrained the RefineNet;
- Constructed the PCCRF training dataset using the training prediction results generated by the trained RefineNet;
- Performed statistical analysis on the training dataset and determined the value of Cgate;
- Initialized the parameters of the PCCRF model; and
- Calculated the parameters of the PCCRF using the method proposed in Zheng et al. [55].
3.5. Experimental Setup
4. Results and Evaluation
5. Discussion
5.1. PCCRF Necessity
5.2. Comparison between PCCRF and FCCRF
5.3. Cgate Effect
5.4. Comparison between PP-CNN and RefineNet-PPCRF
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Number | Name | Description |
---|---|---|
1 | SegNet | Extraction using only SegNet |
2 | SegNet-CRF | Classic CRF post-processing of SegNet results |
3 | SegNet-PCCRF | PCCRF post-processing of SegNet results |
4 | RefineNet | Extraction using only RefineNet |
5 | RefineNet-CRF | Classic CRF post-processing of RefineNet results |
6 | RefineNet-PCCRF | PCCRF post-processing of RefineNet results (method proposed here) |
Hyper-Parameter | Value |
---|---|
Mini-batch size | 32 |
Learning rate | 0.00001 |
Momentum | 0.9 |
Epochs | 30,000 |
Index | SegNet | SegNet-CRF | SegNet-PCCRF | RefineNet | RefineNet-CRF | RefineNet-PCCRF |
---|---|---|---|---|---|---|
Accuracy | 79.01% | 81.31% | 83.86% | 86.79% | 94.01% | 94.51% |
Precision | 76.50% | 78.94% | 80.68% | 85.45% | 91.71% | 92.39% |
Recall | 73.61% | 76.24% | 80.40% | 79.54% | 89.16% | 90.98% |
F1-score | 75.03% | 77.57% | 80.54% | 82.39% | 90.42% | 91.68% |
Index | SegNet | SegNet-CRF | SegNet-PCCRF | RefineNet | RefineNet-CRF | RefineNet-PCCRF |
---|---|---|---|---|---|---|
Time (ms) | 301 | 383 | 315 | 293 | 403 | 313 |
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Share and Cite
Wang, S.; Xu, Z.; Zhang, C.; Zhang, J.; Mu, Z.; Zhao, T.; Wang, Y.; Gao, S.; Yin, H.; Zhang, Z. Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field. Remote Sens. 2020, 12, 821. https://doi.org/10.3390/rs12050821
Wang S, Xu Z, Zhang C, Zhang J, Mu Z, Zhao T, Wang Y, Gao S, Yin H, Zhang Z. Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field. Remote Sensing. 2020; 12(5):821. https://doi.org/10.3390/rs12050821
Chicago/Turabian StyleWang, Shouyi, Zhigang Xu, Chengming Zhang, Jinghan Zhang, Zhongshan Mu, Tianyu Zhao, Yuanyuan Wang, Shuai Gao, Hao Yin, and Ziyun Zhang. 2020. "Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field" Remote Sensing 12, no. 5: 821. https://doi.org/10.3390/rs12050821
APA StyleWang, S., Xu, Z., Zhang, C., Zhang, J., Mu, Z., Zhao, T., Wang, Y., Gao, S., Yin, H., & Zhang, Z. (2020). Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field. Remote Sensing, 12(5), 821. https://doi.org/10.3390/rs12050821