# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 5 Experiments and Results
### Ablation Study  
In this section, we conduct ablation studies to analyze how each component affects the environment pattern recognition performance. We study the following five components of our method: (a) effect of pre-segmentation; (b) effect of data augmentation; (c) effect of pre-training; (d) effect of meta-training; (e) effect of input size. The model used is DenseNet121-PS-MC with the highest recognition accuracy. The test set is Test2, from Anhui Province, and includes one-hundred forty-six samples and five environmental patterns. Table 7 shows the results of the ablation study.  
It is observed that when the pre-segmentation module is removed, the classification performance of the model degrades significantly in different shot settings, and the accuracy decreases by 10.41% in the 3-way 1-shot case. As the pre-segmentation module is removed, the background information in remote sensing images \"spoofs\" the model, making it focus on irrelevant noise. This is especially noticeable in small-size datasets. Data augmentation operations can improve the performance of the model to some extent, but the improvement is limited. Before meta-training, pre-training is introduced to improve the feature representation ability of the model in the case of a few samples, which gives a good initialization of the model; therefore, removing pre-training leads to a decrease in recognition performance. After the meta-training stage is removed, the recognition accuracy of the model decreases by 9.78% in the 3-way1-shot case. This is because meta-training adjusts the scaling parameters in the metric module and optimizes the feature extractor as a way to learn task-level distributions. In addition, the size of the input images also affects the performance of the model, as scaling down inputs loses information. After adjusting the input size to 128 \(\times\) 128, the recognition accuracy decreased by 1.71% in the 3-way 1-shot  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Difference** & **3-Way 1-Shot (\%)** & **3-Way 5-Shot (\%)** \\ \hline default & 85.45 & 90.46 \\ without pre-segmentation & 75.04 & 81.46 \\ without data augmentation & 81.68 & 85.72 \\ without pre-training & 80.71 & 86.25 \\ without meta-training & 75.67 & 81.82 \\
256 \(\times\) 256 input size \(\rightarrow\) 128 \(\times\) 128 input size & 83.74 & 89.69 \\
256 \(\times\) 256 input size \(\rightarrow\) 512 \(\times\) 512 input size & **85.64** & **90.53** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study using DenseNet121-PS-MC on the Test2 test set; “default” indicates the base model, “without” indicates the removal of the component from the base model, “input size” indicates the scale of the input settlement environment map, and the arrow indicates the adjustment made. The best metrics are highlighted in bold font.  
case. In experiments using input images of 512 \(\times\) 512 in size, the recognition accuracy improves by only 0.19% but increases the computational cost exponentially. Therefore, we chose 256 as the final input scale for the model.