# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 3 Methods
### Experimental Setup  
We used the SegNet, U-Net, and RefineNet models for comparison with WFCNN, as their structures and working principles are similar, providing the best test for WFCNN. The models were set up based on previous research, with SegNet containing 13 convolutional layers [39], U-Net containing 10 convolutional layers [42], and RefineNet containing 101 convolutional layers [41]. We implemented WFCNN based on the TensorFlow Framework, using the Python language. To better assess its performance, we also tested two variants, termed WFCNN-1 and WFCNN-2 (Table 4). \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline WFCNN & \\ SegNet & Similar to WFCNN, the classifier used only high-level semantic features. \\ U-Net & Similar to WFCNN, the classifier used fused features. \\ RefineNet & Similar to WFCNN, the linear model was also adopted for feature fusion, but the parameters were all fixed as 1. \\  & The decoding unit was modified to use an adjustment strategy for the feature map depth ascending scale. The length of the feature vector \\  & generated by the decoder was 512 for each pixel. \\  & The decoding unit was modified to remove the weight layer. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Models used in the comparative experiment. SegNet exhibited most errors and the distribution was relatively scattered, with more misclassified pixels in both the edge and inner areas. This shows that it is more reasonable to combine high semantic features with low semantic features than to use only high semantic features. The number of misclassified pixels produced by all three variants of WFCNN was lower for RefineNet and U-net. The excellent performance of RefineNet and U-Net in the camera image indicates that the network structure should be determined in accordance with the spatial resolution of the image. WFCNN performed better than WFCNN-1. This excellent performance indicates that the feature vector dimension was too high and is not conducive to improving the accuracy of the classifier. The result that WFCNN performed better than WFCNN-2 shows that the weight layer played a role. By comparing the performance of U-Net, RefineNet, WFCNN-1, WFCNN-2, and WFCNN, it indicates that different feature fusion methods differ in their contributions to improving accuracy, so it is necessary to choose an appropriate feature fusion method for a given situation. Figure 7: Comparative experimental results for 10 selected images from the aerial image labeling dataset: (**a**) original image, (**b**) manual classification, (**c**) SegNet, (**d**) U-Net, (**e**) RefineNet, (**f**) WFCNN-1, (**g**) WFCNN-2, and (**h**) WFCNN. Figures 8 and 9 present confusion matrices for the different models, which again demonstrate that WFCNN had the best segmentation results. Comparing Figures 8 and 9, it can be found that the performance of each model on the aerial image labeling dataset is better than that on the GF-6 images dataset, indicating that the spatial resolution has a certain impact on the performance of the model. Figure 8: Confusion matrix for different model on the GF-6 images dataset: (**a**) SegNet, (**b**) U-Net, (**c**) RefineNet, (**d**) WFCNN-1, (**e**) WFCNN-2, and (**f**) WFCNN. For comparison, we use Table 5 to summarize the data given in Figure 8, and use Table 6 to summarize the data given in Figure 9.  
We used accuracy, precision, recall, F1-Score, intersection over union (IoU), and Kappa coefficient as indicators to further evaluate the segmentation results for each model (Tables 7 and 8). The F1-Score is defined as the harmonic mean of precision and recall. IoU is defined as the number of pixels labeled as the same class in both the prediction and the reference, divided by the number of pixels labeled as the class in the prediction or the reference. The average accuracy of WFCNN was 18.48 % higher than SegNet, 11.44% higher than U-Net, 7.78% higher than RefineNet, 6.68% higher than WFCNN-1, and 2.15% higher than WFCNN-2.