# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 5 Experiments and Results
### Results of the Proposed Methods  
To address the overfitting problem caused by sparse data, the CNNs are trained using the proposed method. The semantic segmentation model is first trained to extract the settlement environment map. Next, the three CNNs are tuned to accept single-channel maps, naming them AlexNet-PS, ResNet50-PS, and DenseNet121-PS. Following the same configuration, we retrained the three tuned CNNs and obtained the test results in Table 5 and Figure 8.  
As shown in Table 5, all models had improved accuracy once given the settlement environment map as input. ResNet50-PS achieved an average accuracy of 70.37%, a 21.91% enhancement compared to the baseline. This validates the notion that remote sensing images of TCSs contain noise that interferes with the relations between the inputs and the outputs, resulting in the models remembering the noise features instead. The learning curves of the models are featured in Figure 8. This highlights the suppression of the overfitting issue, yet there remains a substantial gap between the validation set and test set accuracy. For example, DenseNet121-PS demonstrated a maximum accuracy of 90% in the validation set, while reaching only 72.13% in the test set. This reflects the inconsistency of the data distributions between the training and test sets. Considering that TCSs have geographical differences, we applied the meta-classification method for mapping the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc1 (\%)** & **Acc2 (\%)** & **mAcc (\%)** \\ \hline AlexNet-PS & 58.06 (+15.54) & 60.96 (+13.70) & 59.51 (+14.62) \\ ResNet50-PS & 68.82 (+22.58) & 71.92 (+21.24) & 70.37 (+21.91) \\ DenseNet121-PS & **72.04** (+18.28) & **75.34** (+17.81) & **73.69** (+18.04) \\ \hline \hline \end{tabular} Note: Acc1 refers to the recognition accuracy of the test set constructed from the TCS data in the Shaanxi Province, while the data in Acc2 are from the Anhui Province; mAcc is the average of the accuracies of the two areas.  
\end{table} 
Table 5: Performance of three CNNs with settlement environment maps on the test set. The best metrics are highlighted in bold font. Figure 7: Accuracy and loss learning curve for the representative CNNs, includes AlexNet, ResNet50, and DenseNet121. input data into a feature vector suitable for comparison, whereby we constructed a series of meta-tasks. The gained models, called AlexNet-PS-MC, ResNet50-PS-MC, and DenseNet121-PS-MC, are each trained with 3-way 1-shot and 3-way 5-shot tasks, each supported by five query samples. After 30 epochs, the highest accuracy model from the validation set was selected for testing, with its accuracy measured as the average of 200 tasks from the test set. In addition, we construct two state-of-the-art few-shot classification models, Meta-Baseline [47] and Meta DeepBDC [49], and adjust them to accept four-channel input data. Both models are trained using the same settings and compared with the proposed method. The recognition results for the Shannxi and Anhui provinces are shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & **3-Way 1-Shot (\%)** & **3-Way 5-Shot (\%)** \\ \hline \multirow{4}{*}{Acc1} & AlexNet-PS-MC & 70.58 (+28.06) & 76.88 (+34.36) \\  & ResNet50-PS-MC & 78.58 (+32.34) & 85.58 (+37.12) \\ \cline{1-1}  & DenseNet121-PS-MC & **81.65** (+27.89) & **87.79** (+34.03) \\ \cline{1-1}  & Meta-Baseline * [47] & 70.53 & 78.24 \\ \cline{1-1}  & Meta DeepBDC * [49] & 72.26 & 80.45 \\ \hline \multirow{4}{*}{Acc2} & AlexNet-PS-MC & 71.63 (+24.37) & 78.74 (+31.48) \\ \cline{1-1}  & ResNet50-PS-MC & 82.57 (+31.89) & 88.32 (+37.64) \\ \cline{1-1} \cline{2-2}  & DenseNet121-PS-MC & **85.45** (+27.92) & **90.46** (+32.93) \\ \cline{1-1}  & Meta-Baseline * [47] & 74.75 & 80.68 \\ \cline{1-1} \cline{2-2}  & Meta DeepBDC * [49] & 76.48 & 83.29 \\ \hline \hline \end{tabular}  
* Note: Acc1 refers to the recognition accuracy of the test set constructed from TCSs data in the Shaanxi Province, while the data in Acc2 are from the Anhui Province.  
\end{table}