Identification and Classification of Small Sample Desert Grassland Vegetation Communities Based on Dynamic Graph Convolution and UAV Hyperspectral Imagery
Abstract
:1. Introduction
- To provide a new high-efficiency and high-precision monitoring method for grassland degradation monitoring. The limitations of manual survey and satellite remote sensing are addressed.
- Propose a spatial neighborhood-based dynamic graph convolution classification algorithm. The classification accuracy of the model is greatly improved under small samples, which is better than other related deep learning classification methods.
- A method for constructing graph structures in the spatial neighborhood is proposed. The method effectively alleviates the large memory consumption and time cost of graph convolutional networks in hyperspectral images.
2. Materials and Methods
2.1. Overview of the Study Area
2.2. Experiment Equipment
2.3. Data Collection
2.4. Data Processing
3. Principle of the Algorithm
3.1. Construction of Spatial Neighborhood Graph
3.2. Edge Convolution Block
3.3. Spatial Neighborhood Dynamic Graph Convolution
4. Experimental Results and Analysis
4.1. Analysis of Experimental Parameters
4.1.1. Analysis of the Numbers of Neighboring Nodes k
4.1.2. Analysis of the Sliding Windows w
4.2. Model Classification Performances Compare Experiments
4.3. Model Comparison Experiment with Different Training Samples
4.4. Feature Visualization Analysis
4.5. Discussion
4.6. Visualization of Experimental Sample Area Classification
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. | Class | Training | Test |
---|---|---|---|
1 | Dominant species | 10 | 1355 |
2 | Constructive species | 10 | 801 |
3 | Companion species | 10 | 745 |
4 | Bare soil | 10 | 274 |
5 | Others | 10 | 234 |
Total | 50 | 3409 |
No. | MLP | 1DCNN | 2DCNN | 3DCNN | Resnet18 | Densenet121 | SN_GCN | SN_DGCN |
---|---|---|---|---|---|---|---|---|
1 | 70.76 ± 0.09 | 70.76 ± 0.10 | 69.98 ± 0.08 | 72.96 ± 0.06 | 75.69 ± 0.04 | 90.20 ± 0.04 | 96.25 ± 0.02 | 98.23 ± 0.01 |
2 | 77.95 ± 0.10 | 71.61 ± 0.12 | 71.89 ± 0.06 | 95.31 ± 0.02 | 96.18 ± 0.03 | 90.94 ± 0.06 | 98.18 ± 0.01 | 98.15 ± 0.01 |
3 | 49.18 ± 0.06 | 46.98 ± 0.10 | 59.95 ± 0.07 | 70.71 ± 0.03 | 74.66 ± 0.11 | 75.60 ± 0.05 | 92.16 ± 0.01 | 94.82 ± 0.01 |
4 | 100.00 ± 0.00 | 99.49 ± 0.01 | 99.27 ± 0.01 | 97.81 ± 0.03 | 97.81 ± 0.02 | 94.60 ± 0.08 | 100.00 ± 0.00 | 100.00 ± 0.00 |
5 | 57.26 ± 0.07 | 73.25 ± 0.07 | 73.67 ± 0.09 | 93.76 ± 0.03 | 83.59 ± 0.06 | 84.62 ± 0.08 | 93.16 ± 0.07 | 91.37 ± 0.06 |
OA | 69.16 ± 0.02 | 68.24 ± 0.03 | 70.84 ± 0.03 | 81.14 ± 0.03 | 82.60 ± 0.02 | 87.15 ± 0.02 | 95.90 ± 0.01 | 97.13 ± 0.01 |
AA | 71.03 ± 0.02 | 72.42 ± 0.01 | 74.95 ± 0.02 | 86.11 ± 0.02 | 85.59 ± 0.02 | 87.19 ± 0.03 | 95.95 ± 0.01 | 96.50 ± 0.01 |
Kappa | 57.77 ± 0.02 | 58.88 ± 0.04 | 60.51 ± 0.04 | 74.77 ± 0.04 | 76.46 ± 0.03 | 82.29 ± 0.03 | 94.37 ± 0.01 | 96.05 ± 0.01 |
No. | DGC-3D-CNN | DIS-O | LGFEN | TAN | SN_DGCN |
---|---|---|---|---|---|
1 | 73.22 ± 0.09 | 81.16 ± 0.07 | 93.83 ± 0.04 | 96.37 ± 0.02 | 98.23 ± 0.01 |
2 | 90.43 ± 0.04 | 82.82 ± 0.05 | 98.30 ± 0.02 | 98.60 ± 0.01 | 98.15 ± 0.01 |
3 | 56.05 ± 0.09 | 62.25 ± 0.09 | 88.08 ± 0.06 | 90.33 ± 0.05 | 94.82 ± 0.01 |
4 | 94.38 ± 0.08 | 100.00 ± 0.09 | 89.41 ± 0.09 | 97.59 ± 0.05 | 100.00 ± 0.00 |
5 | 82.91 ± 0.09 | 75.30 ± 0.05 | 83.76 ± 0.07 | 86.41 ± 0.10 | 91.37 ± 0.06 |
OA | 75.88 ± 0.04 | 78.53 ± 0.02 | 92.57 ± 0.03 | 94.99 ± 0.01 | 97.13 ± 0.01 |
AA | 79.40 ± 0.03 | 80.31 ± 0.01 | 90.68 ± 0.03 | 93.86 ± 0.02 | 96.50 ± 0.01 |
Kappa | 67.41 ± 0.05 | 70.73 ± 0.03 | 89.79 ± 0.04 | 93.09 ± 0.01 | 96.05 ± 0.01 |
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Zhang, T.; Bi, Y.; Zhu, X.; Gao, X. Identification and Classification of Small Sample Desert Grassland Vegetation Communities Based on Dynamic Graph Convolution and UAV Hyperspectral Imagery. Sensors 2023, 23, 2856. https://doi.org/10.3390/s23052856
Zhang T, Bi Y, Zhu X, Gao X. Identification and Classification of Small Sample Desert Grassland Vegetation Communities Based on Dynamic Graph Convolution and UAV Hyperspectral Imagery. Sensors. 2023; 23(5):2856. https://doi.org/10.3390/s23052856
Chicago/Turabian StyleZhang, Tao, Yuge Bi, Xiangbing Zhu, and Xinchao Gao. 2023. "Identification and Classification of Small Sample Desert Grassland Vegetation Communities Based on Dynamic Graph Convolution and UAV Hyperspectral Imagery" Sensors 23, no. 5: 2856. https://doi.org/10.3390/s23052856
APA StyleZhang, T., Bi, Y., Zhu, X., & Gao, X. (2023). Identification and Classification of Small Sample Desert Grassland Vegetation Communities Based on Dynamic Graph Convolution and UAV Hyperspectral Imagery. Sensors, 23(5), 2856. https://doi.org/10.3390/s23052856