# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 4 Results
### The Analysis of Test Results on the UC Merced Dataset
The methods of high-level visual  
Figure 12: Confusion matrix of the UC Merced dataset. features can avoid artificial feature selection and automatically learn potential features from data. Therefore, the classification accuracy of the deep learning methods is generally higher than that of the traditional methods. However, the methods based on deep learning require a lot of data for training, which takes a long time and has high requirements for computer storage. Among deep learning methods such as LPCNN [59], CNN with Overfeat feature [47], WMS-RFPNet, MRNet, MFPNet, WD-RFPNet, WPNP-RFPNet, WMAM-RFPNet, and RFPNet, RFPNet has the highest classification accuracy for the following reasons:  
(1) The multi-view scaling strategy was adopted, and, thus, the CNN model can learn the characteristics of the remote sensing images of different perspectives. On one hand the multi-view scaling strategy is a method of dataset amplification, on the other hand the stretching method in the multi-view scaling strategy is equivalent to adding noise in the process of the inputting data, which can improve the generalization ability of the model. As shown in Table 2, the classification accuracy of RFPNet is 31.66% higher than that of WMS-RFPNet, which proves that the effectiveness of the multi-view scaling strategy for high-resolution remote sensing image scene classification. (2) The adoption of the residual block greatly simplified the learning objective and the training difficulty, and made it possible to deepen the network layer without reducing the accuracy rate. As shown in Table 2, the classification accuracy of RFPNet is 4.52% higher than that of MFPNet, which proves the effectiveness of the adoption of the residual block in scene classification of high-resolution remote sensing images. (3) The fusion strategy of pooling layer feature maps was adopted to cascade the features, which ensured the integrity of information and solved the problem of information loss caused by the pooling operation of traditional CNN. As can be seen in Table 2, the classification accuracy of RFPNet is 5.95% higher than that of MRNet, thus, proving the effectiveness of fusion strategy of pooling layer feature maps. (4) Dropout was adopted to randomly delete some hidden units, thus improving the generalization ability of the model and reducing the phenomenon of overfitting. As can be seen in Table 2, the classification accuracy of RFPNet is 5.95% higher than that of WD-RFPNet, thus proving the effectiveness of using Dropout strategy. (5) The parameter norm penalty strategy was adopted to control the complexity of the model by adding penalty terms to the objective function, so as to solve the problem of overfitting. As can be seen in Table 2, the classification accuracy of RFPNet is 2.38% higher than that of WPNP-RFPNet, thus proving the effectiveness of using parameter norm penalty strategy. (6) The moving average model was adopted to make the final model more robust. As can be seen in Table 2, the classification accuracy of RFPNet is 4.28% higher than that of WMAM-RFPNet, thus proving the effectiveness of using the moving average model. Compared with other deep learning methods like LPCNN [59], CNN with Overfeat feature [47], and so on, the proposed method also has its advantages. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **Method** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{Traditional methods} & SPCK [51] & 73.14 \\  & BOVW [50] & 76.81 \\  & BRSK [52] & 77.80 \\  & SYMK [14] & 75.29 \\ \cline{1-1}  & Bag-of-SIFT [53] & 85.37\(\pm\)1.56 \\ \cline{1-1}  & SPM [54] & 86.8 \\ \cline{1-1}  & SAL-LDA [55] & 88.33 \\ \cline{1-1}  & UFL [56] & 81.67 \\ \cline{1-1}  & MinTree+KD-Tree [57] & 83.1\(\pm\)1.2 \\ \cline{1-1}  & Partlets [58] & 88.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of classification methods based on the UC Merced dataset.