## 3 Experimental Results
### Comparison of Spectral Features, Spatial Features and Fusion Features  
One of the important reasons why the framework can achieve high classification accuracy is the idea of feature fusion. To verify the impact of different features on hyperspectral imagery classification, we use spectral features, spatial features, and fusion features as feature vectors, respectively, to train the SVM classifier and check the differences in classification accuracy. Figure 4 shows the classification results obtained by different feature types for the Pavia University datasets and Kennedy Space Center dataset. When the fusion feature is used as a feature vector, the best classification effect can be achieved. However, when there are only spectral features or spatial features, the classification accuracy decreases to varying degrees. Generally, it is reasonable to believe that when the feature information of a certain category is more fully mined, the resulting higher-dimensional feature vector has a more positive impact on the classification and therefore improves the classification accuracy. For different types of label, the classification accuracy of fusion feature has less fluctuation, which enhances the robustness of the framework.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CLASS** & **SRC-T** & **ELM** & **SVM-RBF** & **CNN** & **FCN** & **Proposed Framework** \\ \hline
1 & 91.20 & 64.45 & 82.21 & 94.97 & 93.58 & **99.19** \\
2 & 96.70 & 80.11 & 77.41 & 96.44 & 96.70 & **99.48** \\
3 & 70.20 & 70.62 & 71.68 & 84.69 & 90.78 & **99.30** \\
4 & 93.40 & 96.40 & 94.76 & 97.39 & 96.72 & **97.53** \\
5 & **100.00** & 98.60 & 99.92 & 99.14 & 88.55 & 99.92 \\
6 & 69.10 & 75.96 & 86.40 & 94.77 & 97.33 & **99.79** \\
7 & 72.40 & 78.20 & 86.25 & 88.90 & 92.38 & **98.90** \\
8 & 77.90 & 79.33 & 89.05 & 84.11 & 90.83 & **96.91** \\
9 & 92.80 & 53.67 & **100.00** & **100.00** & 88.20 & 99.78 \\ \hline OA & 88.70 & 77.76 & 82.49 & 94.35 & 95.11 & **99.10** \\ Kappa & 84.83 & 71.93 & 80.79 & 92.49 & 93.22 & **98.81** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification accuracy (%) for the Pavia university dataset via different algorithms.  
Figure 4: Classification accuracy versus label with fusion feature, spectral feature, and spatial feature for the (**a**) Pavia University dataset and (**b**) Kennedy Space Center dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CLASS** & **SAE-LR** & **Linear SVM** & \begin{tabular}{c} **PCA** \\ **RBF-SVM** \\ \end{tabular} & \
\begin{tabular}{c} **RBF-SVM** \\ \end{tabular} &
\begin{tabular}{c} **Proposed Framework** \\ \end{tabular} \\ \hline OA & 96.73 & 95.52 & 95.35 & 96.51 & **98.47** \\ AA & 94.08 & 91.97 & 91.57 & 93.95 & **98.44** \\ Kappa & 96.36 & 95.01 & 94.82 & 96.11 & **98.29** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy (%) for the Kennedy Space Center dataset via different algorithms.  
The spatial features extracted by the DeepLab v3+ network have a positive impact on subsequent classification. The classification framework based on DeepLab v3+ surpassed CNN, ELM, and other algorithms in this experiment. It extracted the explicit or implicit relationship in space between each category of pixels and other pixels. The spatial features obtained satisfy the requirements of classification tasks, which also indirectly reflects the powerful spatial feature extraction capabilities of DeepLab v3+ and its wide applicability.