## 4 Experimental Result
### Result and Comparison
#### 4.3.1 Train and Test with General datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[PERSON] Model**} & \multicolumn{2}{c}{**Li Model**} & \multicolumn{2}{c}{**[PERSON] Model**} \\ \hline
**\#** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** \\ \hline
1 & 0.000 & 11.06\% & 0.904 & 89.11\% & 0.983 & 96.74\% \\ \hline
2 & 0.002 & 0.18\% & 0.947 & 94.28\% & 1 & 100.00\% \\ \hline
3 & 0.000 & 39.24\% & 0.842 & 82.41\% & 0.993 & 99.72\% \\ \hline
4 & 0.000 & 99.84\% & 0.958 & 94.34\% & 0.993 & 99.92\% \\ \hline
5 & 0.000 & 94.73\% & 0.893 & 90.17\% & 0.995 & 99.50\% \\ \hline
6 & 0.516 & 98.15\% & 0.990 & 98.74\% & 0.996 & 99.27\% \\ \hline
7 & 0.000 & 99.29\% & 0.984 & 97.30\% & 0.995 & 99.22\% \\ \hline
8 & 0.364 & 98.58\% & 0.818 & 80.58\% & 0.92 & 93.66\% \\ \hline
9 & 0.000 & 97.21\% & 0.953 & 95.24\% & 0.998 & 99.77\% \\ \hline
10 & 0.000 & 55.66\% & 0.871 & 84.85\% & 0.978 & 96.07\% \\ \hline
11 & 0.536 & 0.00\% & 0.762 & 71.00\% & 0.974 & 96.36\% \\ \hline
12 & 0.000 & 32.76\% & 0.876 & 86.57\% & 0.989 & 98.04\% \\ \hline
13 & 0.000 & 0.00\% & 0.867 & 84.48\% & 0.99 & 98.42\% \\ \hline
14 & 0.816 & 7.17\% & 0.864 & 83.28\% & 0.987 & 97.92\% \\ \hline
15 & 0.000 & 0.00\% & 0.740 & 72.67\% & 0.867 & 86.37\% \\ \hline
16 & 0.000 & 12.18\% & 0.940 & 90.29\% & 0.947 & 90.77\% \\ \hline OA(\%) & \multicolumn{2}{c}{59.605\%} & \multicolumn{2}{c}{86.512\%} & \multicolumn{2}{c}{95.269\%} \\ \hline AA(\%) & \multicolumn{2}{c}{46.63\%} & \multicolumn{2}{c}{87.21\%} & \multicolumn{2}{c}{96.99\%} \\ \hline Kappa & \multicolumn{2}{c}{0.539} & \multicolumn{2}{c}{0.850} & \multicolumn{2}{c}{0.947} \\ \hline  & \multicolumn{2}{c}{**Chen Model**} & \multicolumn{2}{c}{**Hu Model**} & \multicolumn{2}{c}{**Proposed Model**} \\ \hline
**\#** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** \\ \hline
1 & 0.963 & 80.20\% & 0.985 & 100\% & 0.967 & 93.68\% \\ \hline
2 & 0.911 & 98.87\% & 0.989 & 97.95\% & 0.980 & 99.69\% \\ \hline
3 & 0.515 & 94.83\% & 0.930 & 95.88\% & 0.989 & 98.99\% \\ \hline
4 & 0.427 & 92.83\% & 0.988 & 98.73\% & 0.988 & 97.66\% \\ \hline
5 & 0.699 & 93.40\% & 0.959 & 93.56\% & 0.983 & 98.66\% \\ \hline
6 & 0.959 & 91.05\% & 0.999 & 99.94\% & 0.998 & 99.94\% \\ \hline
7 & 0.902 & 91.24\% & 0.993 & 99.13\% & 0.999 & 100\% \\ \hline
8 & 0.739 & 84.39\% & 0.761 & 78.94\% & 0.862 & 80.6\% \\ \hline
9 & 0.636 & 96.53\% & 0.992 & 99.09\% & 0.995 & 99.04\% \\ \hline
10 & 0.856 & 88.85\% & 0.890 & 85.64\% & 0.967 & 97.52\% \\ \hline
11 & 0.908 & 87.72\% & 0.908 & 92.54\% & 0.966 & 98.38\% \\ \hline
12 & 0.748 & 86.97\% & 0.983 & 97.34\% & 0.986 & 97.31\% \\ \hline
13 & 0.966 & 89.21\% & 0.964 & 96.25\% & 0.973 & 95.34\% \\ \hline
14 & 0.842 & 86.81\% & 0.954 & 95.81\% & 0.964 & 97.15\% \\ \hline
15 & 0.332 & 99.29\% & 0.667 & 64.73\% & 0.744 & 85.93\% \\ \hline
16 & 0.642 & 39.54\% & 0.979 & 97.68\% & 0.994 & 99.81\% \\ \hline OA(\%) & \multicolumn{2}{c}{87.005\%} & \multicolumn{2}{c}{88.66\%} & \multicolumn{2}{c}{92.94\%} \\ \hline AA(\%) & \multicolumn{2}{c}{87.61\%} & \multicolumn{2}{c}{93.32\%} & \multicolumn{2}{c}{96.23\%} \\ \hline Kappa & \multicolumn{2}{c}{0.857} & \multicolumn{2}{c}{0.874} & \multicolumn{2}{c}{0.9212} \\ \hline \end{tabular}
\end{table} 
Table 7: Classification results of the Salinas dataset to which random spatial information is not applied. It was confirmed that spatial-spectral feature extraction by 3D-Convolution through the YU Paint data has little effect on data with too little spatial information. In the data, if it is very small, the effect is not great. Through this, it was confirmed that the performance of our proposed ODPA-CNN is very good for very limited data and new data.