## 6 Results and Discussion
### Overall Accuracy of the Different Models  
As shown in Tables 2-4, the overall accuracy of the CAE_CNN model is 0.944, which is higher than that of the CAE_SMX model (overall accuracy of 0.20) and the Best_CNN model (overall accuracy of 0.916). The classification accuracy of each category based on the CAE_CNN model is higher than or close to 90%. These results show that the CAE_CNN model has a significant advantage in object-oriented remote sensing classification compared to the CAE_SMX and Best_CNN models.  
The loss function values of the CAE part have reached 0.0435 (as shown by the red line in Figure 8), which indicates that the feature maps extracted using the CAE are very effective and accurately represent the original image. Nevertheless, the overall accuracy of the CAE_SMX model is still only 0.2. These results indicate that the CAE is suitable for feature extraction and data dimension reduction but not direct classification.  
The overall accuracy of the Best_CNN model is 0.916; this is not a relatively good accuracy, which indicates that the CNN has difficulty in achieving high accuracy results when classifying original  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Road** & **Forest** & **Green Space** & **Water Body** & **Residence** & **Total** \\ \hline Road & 84 & 1 & 13 & 0 & 2 & 100 \\ Forest & 0 & 95 & 5 & 0 & 0 & 100 \\ Green space & 0 & 17 & 83 & 0 & 0 & 100 \\ Water body & 1 & 0 & 0 & 99 & 0 & 100 \\ Residence & 3 & 0 & 0 & 0 & 97 & 100 \\ Total & 88 & 113 & 101 & 99 & 99 & 500 \\ Producer’s accuracy & 0.954 & 0.841 & 0.822 & 1 & 0.980 & \\ Overall accuracy & 0.916 & & & & & \\ Kappa value & 0.895 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The confusion matrix of the Best_CNN model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Road** & **Forest** & **Green Space** & **Water Body** & **Residence** & **Total** \\ \hline Road & 91 & 3 & 6 & 0 & 0 & 100 \\ Forest & 1 & 97 & 2 & 0 & 0 & 100 \\ Green space & 1 & 11 & 88 & 0 & 0 & 100 \\ Water body & 0 & 0 & 0 & 100 & 0 & 100 \\ Residence & 4 & 0 & 0 & 0 & 96 & 100 \\ Total & 97 & 111 & 96 & 100 & 96 & 500 \\ Producer’s accuracy & 0.938 & 0.874 & 0.917 & 1.000 & 1.000 & \\ Overall accuracy & 0.944 & & & & & \\ Kappa value & 0.930 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrix of the CAE_CNN model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Road** & **Forest** & **Green Space** & **Water Body** & **Residence** & **Total** \\ \hline Road & 91 & 3 & 6 & 0 & 0 & 100 \\ Forest & 1 & 97 & 2 & 0 & 0 & 100 \\ Green space & 1 & 11 & 88 & 0 & 0 & 100 \\ Water body & 0 & 0 & 0 & 100 & 0 & 100 \\ Residence & 4 & 0 & 0 & 0 & 96 & 100 \\ Total & 97 & 111 & 96 & 100 & 96 & 500 \\ Producer’s accuracy & 0.938 & 0.874 & 0.917 & 1.000 & 1.000 & \\ Overall accuracy & 0.944 & & & & & \\ Kappa value & 0.930 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The confusion matrix of the CAE_SMX model.  
objects directly, and the accuracy of the training set of the Best_CNN model reaches 0.998, indicating that the model is overfitted trained on the given data.  
This analysis indicates that the CAE_CNN model combines the advantages of both the CAE and CNN to accurately classify the data, and the CAE_CNN model can overcome the problem that it is easy for traditional CNNs to fall into over-fitting to some extent in the case of limited numbers of labelled samples.