# Research on Two-Dimensional Digital Map Modeling Method Based on UAV Aerial Images
## 4 Experimental Validation
### Feature Extraction and Registration  
To perform point feature-based image registration on high signal-to-noise ratio images, feature extraction is the first step. Two images captured by a drone were randomly selected, and the same preprocessing method was applied for noise reduction. With the threshold for the number of features set to 3000, the results of feature extraction using the SIFT algorithm, SURF algorithm, and C-SURF algorithm are shown in Figures 6-8. After applying the SIFT algorithm for feature extraction on the drone-captured images, instances of feature point omission were observed at the location of the crosswalk in Figure 6a and at the edge of the flowerbed in Figure 6b. According to the detection results shown in Figure 7, the feature extraction results of the SURF algorithm also exhibit the same issue. Instances of feature point omission are observed at the location of the crosswalk in Figure 7a and at the edge of the flowerbed in Figure 7b. Based on the principles of the C-SURF algorithm, feature extraction was performed on the collected images, and the experimental results are shown in Figure 8. The results indicate that the issue of feature point omission has been effectively addressed. Additionally, Table 1 compares the feature extraction results of the SIFT algorithm, SURF algorithm, and C-SURF algorithm across four dimensions: set detection quantity, actual detection quantity, effective detection quantity (the actual number of detections quantity divided by the set number of detections quantity), and time consumption. From Table 1, it can be observed that the C-SURF algorithm demonstrates a low dependency of the number of detected effective features on the complexity of the scene within the images. When the threshold for the number of feature detections is set to 3000, the feature detection rate can reach over 90% of the threshold. Therefore, the C-SURF algorithm exhibits a greater advantage in detecting feature points compared to the SIFT and SURF algorithms. Figure 5: The result of image denoising. Figure 6: The results of SIFT feature extraction: (**a**) extraction result 1, (**b**) extraction result 2. Figure 7: The results of SURF feature extraction: (**a**) extraction result 1, (**b**) extraction result 2. During the feature registration phase, the feature descriptors of the C-SURF algorithm have an increased dimensionality of 136 dimensions, compared to 128 dimensions for the SIFT algorithm and 64 dimensions for the SURF algorithm, resulting in higher registration accuracy. The feature registration pairs of the three algorithms are shown in Table 2, where the registration accuracy rate is obtained by dividing the fine matching point pair/group by the coarse matching point pair/group. In the feature registration phase, the C-SURF algorithm employs a brute-force matching method for coarse matching, followed by the use of the Random Sample Consensus algorithm for filtering. The feature matching results after this process show an improvement of nearly 17% compared to the feature matching results based on the SIFT algorithm and an improvement of nearly 15% compared to the matching results of the SURF feature extraction algorithm. Additionally, the specific feature matching results are illustrated in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Extraction** & **Set Detection** & **Actual Detection** & **Effective Detection** & **Time Consumption** \\
**Algorithm** & **Quantity** & **Quantity** & **Quantity (\%)** & **(Seconds)** \\ \hline SIFT feature & & & & \\ extraction result 1 & 3000 & 1842 & 61.4\% & 2.235 s \\ SIFT feature & & & & \\ extraction result 2 & & & & \\ SURF feature & & & & \\ extraction result 1 & 3000 & 1421 & 47.4\% & 1.876 s \\ SURF feature & & & & \\ extraction result 2 & & & & \\ C-SURF feature & & & & \\ extraction result 2 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of feature extraction results detected by the three algorithms.