## 3 Experimental Results
### Visualization  
To intuitively understand the impact of different factors on the classification, we also have done a series of data visualization. First, we use the visualization tool TSNE to reduce the dimensionality of the feature vector of each pixel to two dimensions, and project it to a 2-D coordinate system. By observing the degree of dispersion in each category, we can judge the difficulty of classification.  
Figure 5 represents the distribution effects of pixels with spectral, spatial, and fusion features for the Pavia University dataset, Kennedy Space Center dataset, and HyRANK  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**[PERSON]**} & \multicolumn{3}{c}{**[PERSON]**} \\ \cline{2-10}
**Class** & **KNN** & \begin{tabular}{c} **Logistic** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Decision** \\ **Tree** \\ \end{tabular} & \begin{tabular}{c} **Naive Bayesian** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **SVM** \\ **KNN** \\ \end{tabular} & \begin{tabular}{c} **Logistic** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Decision** \\ **Tree** \\ \end{tabular} & \
\begin{tabular}{c} **Naive Bayesian** \\ **Model** \\ \end{tabular} &
\begin{tabular}{c} **SVM** \\ **Model** \\ \end{tabular} \\ \hline
1 & 68.06 & 40.15 & 18.88 & 18.03 & 67.82 & 85.66 & 79.19 & 49.04 & 40.40 & 92.81 \\
2 & 100 & 100 & 50 & 100 & 100 & 93.89 & 94.89 & 31.33 & 100 & 100 \\
3 & 83.81 & 73.03 & 51.45 & 40.94 & 90.4 & 98.55 & 74.14 & 49.58 & 54.10 & 95.73 \\
4 & 59.32 & 72.73 & 10.87 & 100 & 100 & 81.44 & 88.89 & 19.37 & 78.26 & 100 \\
5 & 81.49 & 66.11 & 45.29 & 35.73 & 82.87 & 94.63 & 78.60 & 61.87 & 62.60 & 94.57 \\
6 & 81.48 & 46.07 & 32.02 & 15.47 & 100 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\
7 & 77.33 & 66.67 & 39.32 & 40.34 & 94.08 & 97.43 & 92.14 & 81.95 & 63.89 & 100 \\
8 & 72.98 & 67.16 & 50.46 & 53.84 & 82.19 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\
9 & 74.22 & 71.08 & 62.85 & 80.10 & 78.02 & 97.26 & 90.82 & 87.79 & 88.45 & 95.34 \\
10 & 83.06 & 69.7 & 63.53 & 73.31 & 73.2 & 96.78 & 87.90 & 83.64 & 87.34 & 93.84 \\
11 & 89.66 & 76.69 & 49.78 & 49.40 & 96.25 & 95.42 & 84.01 & 60.58 & 72.53 & 90.79 \\
12 & 97.31 & 84.54 & 69.60 & 57.93 & 95.51 & 99.55 & 96.84 & 82.33 & 88.99 & 100 \\
13 & 100 & 99.60 & 95.91 & 100 & 100 & 98.51 & 99.66 & 97.89 & 99.04 & 100 \\
14 & 100 & 99.01 & 79.33 & 91.82 & 100 & 99.70 & 96.82 & 91.74 & 59.77 & 100 \\ \hline OA & 81.45 & 73.92 & 60.70 & 52.76 & 82.39 & 96.06 & 88.00 & 77.26 & 76.90 & 94.92 \\ AA & 83.48 & 73.75 & 51.38 & 61.21 & 90.03 & 94.90 & 88.66 & 66.43 & 74.61 & 96.92 \\ Kappa & 77.80 & 68.72 & 53.43 & 47.66 & 78.69 & 95.10 & 85.06 & 71.92 & 71.80 & 93.66 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification accuracy (%) for Loukia and Dioni.  
Figure 8: Feature maps produced using SVM classifiers and fusion features for the HyRANK dataset. (**a**) The ground truth of Loukia. (**b**) The predicted feature map for Loukia. (**c**) The ground truth of Dioni. (**d**) The predicted feature map for Dioni.  
Figure 7: Feature maps achieved by using different classifiers and features for the Kennedy Space Center dataset. (**a**) DT—fusion feature, (**b**) KNN—fusion feature, (**c**) LR—fusion feature, (**d**) NBM—fusion feature; (**e**) SVM—fusion feature; (**f**) SVM—spatial feature; and (**g**) SVM—spectral feature.