# An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
## 2 Materials and Methods
### The Building Method of Simple Stable Feature Database
#### 2.2.2 Iterative Matching Strategy for Feature Database Methods
A high feature matching number indicates that the feature class appears more times in the training iteration process, while a low feature matching number indicates that the feature class successfully matches fewer times in the training process. In this paper, the match number of feature classes is used as the standard for filtering stable features. The value of \(\mathit{FMN}\) (filter match number) is used to filter stable features in the feature database. The value of \(\mathit{FMN}\) is a key parameter that determines the number and stability of feature classes obtained from the feature database. For example, \(\mathit{FMN}=6\) indicates that the feature classes with the number of matches greater than or equal to 6 are extracted from the feature database. The \(\mathit{FMN}\) of the simple stable feature database needs to be determined by a filtering experiment of matching times. The value of \(\mathit{FMN}\) is determined by the observation of the correct matching rate curve in the experiment. \(\mathit{FMN}\) needs to ensure that the feature database has a certain number of feature classes obtained by filtering and yet can be matched with images to obtain a high correct matching rate. Figure 2: Flow chart of feature database training. ## 3 Experiments and Results

In this section, we build several feature databases based on different feature extraction algorithms and train the feature databases according to the method proposed in Section 2.2. We first set up different \(FMN\) filtering feature classes from the feature database and evaluate the performance of the proposed feature database matching method by the matching effect of the filtering results with the test images. Then, the feature database matching methods based on different feature algorithms are compared with the reference image matching methods to verify the efficiency and accuracy of the feature database matching methods. ### Evaluation Indicators

Three quantitative metrics are employed in this experiment section to evaluate how the proposed feature database matching method performs. 1. The stability of the feature matching method is measured by the correct matching ratio (\(CMR\)). It is the proportion of the number of correct matches (\(NCM\)) to the total number of feature matches (\(Nt\)). Because a feature class in the feature database matching method contains multiple similar features and a successful match of one of the features means a successful match of this feature class, the calculation of the \(CMR\) matching method of the feature database is based on the feature classes described in this paper. \[CMR=\frac{NCM}{Nt}\] (1)
2. Root mean square error (\(RMSE\)), which is used to reflect the geometric localization accuracy of the feature matching method, where \((x_{i}^{r},y_{i}^{r})\) denotes the coordinates of the matched feature points in the reference image or feature database and \((x_{i}^{r^{\prime}},y_{i}^{r^{\prime}})\) denotes the corresponding coordinates of the matched points in the test image after geometric correction. A smaller \(RMSE\) denotes a higher degree of geometric localization accuracy using the feature-matching approach. \[RMSE=\sqrt{\frac{1}{NCM}\sum_{i=1}^{NCM}\left((x_{i}^{r}-x_{i}^{r^{\prime}})^{ 2}+(y_{i}^{r}-y_{i}^{r^{\prime}})^{2}\right)}\] (2)
3. The total time (\(Time\)) spent to extract features and perform feature matching from the reference image or feature database and the test image, which is used to reflect the matching efficiency of the feature matching method. ### Experimental Data

To test the feature database matching method, a data set is used in the experiments. The data set takes the western area of Beijing as the experimental area and contains one reference image, fifty training images, and nine test images. The reference image is a large-scale Google image with an accurate geographic location, and the image range is the latitude and longitude range of the feature database.