# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 4 Results
### The Analysis of Test Result on the SIRI-WHU Dataset  
In order to prove the effectiveness of the proposed method, the experiment was also carried out on the SIRI-WHU dataset. The SIRI-WHU dataset [60] is Google's 12 class image dataset. It was collected by the remote sensing group of Wuhan university from Google Earth and mainly covers urban areas in China. The size of each image is 200 \(\times\) 200 pixels and the spatial resolution is 2 m. Each category of the dataset has 200 images, as shown in Figure 13. Since satellite images in Google Earth are not a single data source, but the integration of satellite images and aerial data, the images are different from those in the UC Merced dataset. Multiple indicators are used to evaluate the result on the SIRI-WHU dataset, as shown in Figure 14. In addition to meadow, the accuracy, precision, recall, and F1-score of other categories all exceed 80%. The average accuracy, average precision, average recall and average F1-score of all categories are 91.5%, 92%, 91%, and 91%, respectively, which further proves that the proposed method has comprehensive advantages. The confusion matrix of the SIRI-WHU dataset is shown in Figure 15a, from which we can see that the classification accuracy of a single category ranged from 77% to 97%. The categories with 97% classification accuracy are industrial, residential, water, and overpass, which further proves the high effectiveness of the proposed method. The reason for the accuracy of SIRI-WHU dataset is lower than that of UCM dataset is analyzed as follows: as can be seen in Figure 15a, the classification accuracy of pond was only 80% and 10% pictures are misclassified as rivers. Worse still, the meadow classification accuracy is the lowest among other categories and 12% pictures were misclassified into parks. As can be seen in Figure 15b, the similarity between ponds and rivers leads to low classification accuracy. As for meadow and park, the park is basically composed of meadow, there is little difference between the two categories. In a word, the SIRI-WHU dataset is challenging due to the high similarity between classes, and the good results on the SIRI-WHU dataset indicate the feasibility of the proposed method. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **Method** & **Accuracy (\%)** \\ \hline \multirow{6}{*}{Deep learning methods} & LPCNN [59] & 89.9 \\  & CNN with Overfeat feature [47] & 92.4 \\  & WMS-RFPNet & 61.67 \\  & MFPNet & 88.81 \\  & MRNet & 87.38 \\  & WD-RFPNet & 87.38 \\  & WFNP-RFPNet & 90.95 \\  & WMAM-RFPNet & 89.05 \\  & **RFPNet** & **93.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 13: Sample of 12 class Google image dataset of SIRI-WHU: (**a**) water; (**b**) river; (**c**) residential; (**d**) pond; (**e**) park; (**f**) overpass; (**g**) meadow; (**h**) industrial; (**i**) idle land; (**j**) harbor; (**k**) commercial; (**I**) agriculture. In order to prove the effectiveness of the proposed method, the accuracy of the proposed method is compared with traditional methods and deep learning methods on the SIRI-WHU dataset. As accuracy is the general evaluation index of other comparison methods, this paper only compares the accuracy of different methods. As shown in Table 3, the method proposed in the paper on the SIRI-WHU dataset has achieved good results. Traditional methods mentioned in this paper also refer to the scene classification methods of high-resolution remote sensing images based on low-level visual features and middle-level visual features. SPM [14], LDA [17], PLSA [61], S-UFL [62], SIFT+BoVW [63], and RF [64] are all high-resolution remote sensing scene classification methods based on middle-level visual features, and their accuracy is mostly lower than that of deep learning methods, let alone the methods of low-level visual features.