## 4 Experimental Result
### Result and Comparison
#### 4.3.3 Trained with General datasets and Tested with Random Spatial datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Luo Model**} & \multicolumn{2}{c}{**Li Model**} & \multicolumn{2}{c}{**Hamida Model**} \\ \hline
**\#** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** \\ \hline
1 & 0.000 & 0.00\% & 0.000 & 0.00\% & 0.000 & 0.00\% \\ \hline
2 & 0.029 & 1.48\% & 0.000 & 0.00\% & 0.089 & 5.07\% \\ \hline
3 & 0.067 & 8.76\% & 0.015 & 0.76\% & 0.018 & 0.96\% \\ \hline
4 & 0.048 & 2.94\% & 0.008 & 0.43\% & 0.003 & 0.14\% \\ \hline
5 & 0.082 & 5.97\% & 0.053 & 3.25\% & 0.039 & 2.65\% \\ \hline
6 & 0.012 & 0.63\% & 0.052 & 2.80\% & 0.219 & 14.85\% \\ \hline
7 & 0.063 & 4.36\% & 0.000 & 0.00\% & 0.072 & 4.47\% \\ \hline
8 & 0.370 & 73.33\% & 0.336 & 54.64\% & 0.368 & 54.68\% \\ \hline
9 & 0.102 & 5.59\% & 0.060 & 3.77\% & 0.192 & 14.19\% \\ \hline
10 & 0.133 & 22.94\% & 0.173 & 24.86\% & 0.142 & 10.37\% \\ \hline
11 & 0.000 & 0.00\% & 0.014 & 0.84\% & 0.030 & 2.25\% \\ \hline
12 & 0.040 & 4.88\% & 0.075 & 7.58\% & 0.083 & 10.59\% \\ \hline
13 & 0.000 & 0.00\% & 0.049 & 20.63\% & 0.045 & 27.07\% \\ \hline
14 & 0.021 & 3.46\% & 0.024 & 6.92\% & 0.033 & 9.35\% \\ \hline
15 & 0.000 & 0.00\% & 0.163 & 13.11\% & 0.132 & 9.42\% \\ \hline
16 & 0.000 & 0.00\% & 0.022 & 1.16\% & 0.061 & 3.76\% \\ \hline OA(\%) & 18.668\% & \multicolumn{2}{c}{16.292\%} & \multicolumn{2}{c}{17.996\%} \\ \hline AA(\%) & 8.40\% & \multicolumn{2}{c}{8.80\%} & \multicolumn{2}{c}{10.61\%} \\ \hline Kappa & 0.0469 & \multicolumn{2}{c}{0.0432} & \multicolumn{2}{c}{0.0753} \\ \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 & 0.000 & 0.00\% & 0.971 & 97.76\% & 0.9988 & 99.75\% \\ \hline
2 & 0.051 & 3.08\% & 0.980 & 97.45\% & 0.9973 & 99.95\% \\ \hline
3 & 0.028 & 2.05\% & 0.871 & 80.97\% & 0.9832 & 97.82\% \\ \hline
4 & 0.000 & 0.00\% & 0.979 & 98.21\% & 0.9932 & 99.00\% \\ \hline
5 & 0.056 & 5.59\% & 0.923 & 96.86\% & 0.9860 & 99.70\% \\ \hline
6 & 0.110 & 26.58\% & 0.997 & 99.67\% & 0.9990 & 99.80\% \\ \hline
7 & 0.000 & 0.00\% & 0.975 & 98.71\% & 0.9976 & 99.58\% \\ \hline
8 & 0.090 & 22.38\% & 0.774 & 94.28\% & 0.8598 & 85.36\% \\ \hline
9 & 0.000 & 0.00\% & 0.963 & 97.37\% & 0.9944 & 99.53\% \\ \hline
10 & 0.002 & 0.10\% & 0.854 & 81.73\% & 0.9555 & 94.69\% \\ \hline
11 & 0.094 & 5.42\% & 0.751 & 72.94\% & 0.9653 & 98.97\% \\ \hline
12 & 0.000 & 0.00\% & 0.946 & 99.90\% & 0.9979 & 99.95\% \\ \hline
13 & 0.038 & 2.93\% & 0.938 & 98.91\% & 0.9851 & 97.60\% \\ \hline
14 & 0.035 & 1.82\% & 0.927 & 90.37\% & 0.9718 & 98.13\% \\ \hline
15 & 0.000 & 0.00\% & 0.401 & 26.50\% & 0.7933 & 80.09\% \\ \hline
16 & 0.000 & 0.00\% & 0.950 & 92.81\% & 0.9922 & 98.84\% \\ \hline OA(\%) & 5.385\% & \multicolumn{2}{c}{85.281\%} & \multicolumn{2}{c}{93.589\%} \\ \hline AA(\%) & 4.37\% & \multicolumn{2}{c}{89.03\%} & \multicolumn{2}{c}{96.80\%} \\ \hline Kappa & 0.0067 & \multicolumn{2}{c}{0.8348} & \multicolumn{2}{c}{0.9286} \\ \hline \end{tabular}
\end{table} 
Table 14: Classification results of the Salinas dataset to which random spatial information is not applied for training and random spatial information to test. Through the above experiments, it can be seen that 3D-CNN models rely heavily on spatial information. It can be seen that 3D-CNN may overfit with excessive feature extraction. Therefore, 1D-CNN, which classifies classes only with spectral information of an object, is more advantageous than 3D-CNN in a situation where there is little spatial information.