## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.2.3 Validity Analysis of the SSECRF Algorithm  
For the classification task, the visual performance is of importance for the proposed method, so the final classification map obtained by the SSECRF algorithm is shown in Figure 5. The classification map exhibits a good appearance in some classes, except for the misclassification between trees and vegetation. It preserves the more useful information and achieves a complete shape feature with fewer meaningless regions and less salt-and-pepper classification noise. However, the over-fitting phenomenon can be observed in the road area. On the whole, the proposed SSECRF algorithm shows a pleasing visual performance. At the same time, the quantitative confusion matrix can also reflect the performance of the classification result. The given test samples were used to evaluate the effectiveness of the proposed method, and the corresponding quantitative results are reported in Table 5. The class accuracies are all more than 90%, except for the trees and vegetation, which are confused. In summary, the quantitative evaluation result shows an effective classification performance after the fusion of the visible and thermal infrared hyperspectral imagery by SSECRF. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Road** & **Trees** & **Red Roof** & **Grey Roof** & **Concrete Roof** & **V Vegetation** & **Bare Soil** \\ \hline Road & 92.9 & 0.19 & 0.03 & 2.26 & 0.72 & 1.16 & 0.5 \\ Trees & 0.04 & 89.23 & 0.1 & 0 & 0.01 & 19.97 & 0 \\ Red roof & 0.01 & 0.02 & 95.89 & 3.48 & 0.01 & 0.01 & 3.13 \\ Grey roof & 5.11 & 0.13 & 2.85 & 90.6 & 4.83 & 0.05 & 0.3 \\ Concrete roof & 1.83 & 0 & 0.11 & 3.65 & 94.37 & 0.01 & 0.39 \\ Vegetation & 0.01 & 10.35 & 0.12 & 0 & 0 & 78.46 & 1.96 \\ Bare soil & 0.1 & 0.07 & 0.91 & 0 & 0.05 & 0.35 & 93.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy assessment of the classification results obtained with the SSE feature set. Figure 5: The classification map obtained by the SSECRF algorithm. The representative areas are selected to show the details of the classification results in some specific classes. The black boxes 1 and 2 contain some roads, roofs and vegetation. A baseball field can be obviously seen in black box 3, and a large-area mixture of concrete roof and road is shown in black box 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Road** & **Trees** & **Red Roof** & **Grey Roof** & **Concrete Roof** & **V Vegetation** & **Bare Soil** \\ \hline Road & 94.58 & 0.01 & 0 & 2.30 & 0.25 & 0.94 & 0.80 \\ Trees & 0 & 91.42 & 0.07 & 0 & 0 & 12.89 & 0 \\ Red roof & 0 & 0 & 97.39 & 3.04 & 0 & 0 & 0.21 \\ Grey roof & 4.80 & 0.03 & 2.44 & 94.66 & 4.19 & 0 & 0 \\ Concrete roof & 0.62 & 0 & 0 & 0 & 95.56 & 0 & 0 \\ Vegetation & 0 & 8.54 & 0 & 0 & 0 & 86.16 & 0.05 \\ Bare soil & 0 & 0 & 0.20 & 0 & 0 & 0 & 98.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy assessment of the classification map obtained by SSECRF. In the proposed algorithm, the SSE feature set is used to model the unary potential. In Section 4.2.2, the SSE feature set was input into SVM (SSE-SVM) to obtain the classification map, obtaining a result that was better than the results obtained using the SSR feature set. From the confusion matrices, it can be seen that CRF performs well in distinguishing all the classes, especially grey roof, vegetation, and bare soil. At the same time, other experiments were conducted with SSECRF using the different feature sets to model the unary potential term. The spatial-spectral feature set from the visible imagery and the emissivity information used separately in the SSECRF algorithm are denoted as SS-CRF and E-CRF. A comparison of the subset classification maps is shown in Figure 6, where it can be observed that the SSE-SVM results contain much salt-and-pepper classification noise, and the SS-CRF and E-CRF results lose much detailed information.