# 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 UC Merced dataset [50] collected by the United States Geological Survey (USGS) is used to prove the effectiveness of the proposed method. It contains 21 kinds of land scene with 100 images in each category and covers various urban areas around the United States, as shown in Figure 10. The size of each image is 256 \(\times\) 256 pixels and the spatial resolution is about 30 cm. The UC Merced dataset presents highly overlapping categories, such as \"dense residential area\", \"sparse residential area\", and \"medium residential area\", which further increases the difficulty of classification. Multiple indicators are used to evaluate the result, as shown in Figure 11. The accuracy, precision, recall, and F1-score of each category all exceed 80%. The average accuracy, average precision, average recall, and average F1-score of all categories are 93.3%, 94%, 93%, and 93%, respectively, which proves that the proposed method has comprehensive advantages. The confusion matrix of the UC Merced dataset is shown in Figure 12, from which we can see that the classification accuracy of a single category ranged from 85% to 100%. The categories with 100% classification accuracy are runway, parking lot, mobile home park, chaparral, beach, harbor, and freeway, which further proves the high performance of the proposed method. Figure 11: Test result of accuracy, precision, recall, F1-score on the UC Merced dataset. Figure 10: Sample of 21 class UC Merced dataset: (**a**) tennis court; (**b**) storage tanks; (**c**) sparse residential; (**d**) runway; (**e**) river; (**f**) parking lot; (**g**) overpass; (**h**) mobile home park; (**i**) medium residential; (**j**) intersection; (**k**) harbor; (**l**) golf course; (**m**) freeway; (**n**) forest; (**o**) dense residential; (**p**) chaparral; (**q**) buildings; (**r**) beach; (**s**) baseball diamond; (**t**) airplane; (**u**) agricultural. 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 UC Merced dataset. Different evaluation indexes were used in the articles for comparison, but accuracy was used as their general evaluation index. Therefore, in order to compare with other methods, the paper only compares the accuracy so as to illustrate the advantages of the proposed method. As shown in Table 2, the method proposed in the paper has achieved good results. Traditional methods mentioned in this paper refer to the scene classification methods of high-resolution remote sensing images based on the low-level visual features and the middle-level visual features. SPCK [51], BOVW [50], BRSK [52], SPMK [14], Bag-of-SIFT [53], SPM [54], SAL_LDA [55], UFL [56], MinTree+KD-Tree [57] and Partlets [58] all belong to the scene classification methods of high-resolution remote sensing images based on middle-level visual features, and their accuracy is almost lower than that of the deep learning methods, let alone the methods based on low-level visual features. Low-level visual features have certain limitations in the semantic information description of scene classification. Compared with the classification methods based on low-level features and high-level features, although the implementation process of the classification methods based on low-level visual features is relatively simple, the classification accuracy is the lowest. The presentation of middle-level visual features bridged the gap between low-level features and semantic categories of scenes. Compared with the classification methods based on low-level features, the classification methods based on middle-level features can improve the classification accuracy, but the implementation process will be more complex. Compared with the classification methods based on high-level features, the methods based on middle-level visual features still have some problems in the expression of semantic categories.