# 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
The training set contains 50 high-resolution remote sensing images of Gaofen-2 (GF-2), these remote sensing images are basically within the set feature database area, overlapping each other, and are used for feature database iterative training. The test set is selected from three images of Jilin-1 (JL-1), Gaofen-1 (GF-1), and GF-2 remote sensing images, respectively, which are used to match with the trained feature database. The latitude and longitude of the test images are also basically within the matching range of the feature database. To ensure the training of the feature database and subsequent geometric localization, we pre-aligned the training images in the two input remote sensing datasets. The interpolation algorithm first unifies the multiple-resolution images in this paper into a single resolution before matching and comparing them. Because remote sensing images can be based on a uniform scale of geographic information, matching on the same scale can improve the correct rate of matching and avoid incorrect matching due to different scales. ### Large Area Remote Sensing Image Feature Database Matching Experiment

#### 3.3.1 Matching Times Filtering Experiment

First, the feature database update parameters \(\mathit{thre1}=0.5\), \(\mathit{thre2}=4\) are set empirically, and several feature databases based on different feature operators are trained according to the method described in Section 2.2 in combination with the reference image and training set. To ensure the uniformity of matched feature points in large regions, a block processing strategy is adopted in the feature database feature extraction, training process, and the subsequent matching process with the test set. The training process ensures that different feature algorithms extract the same number of features for the same training image for subsequent experimental comparison. After obtaining several feature databases based on the training set \(B\) and different feature extraction algorithms, the test set \(C\) is matched with the stable feature classes extracted from these feature databases using the \(\mathit{FMN}\). There are a total of 50 training images available, and the ideal situation is that there are enough feature classes with a matching count of 50, that is, multiple feature classes are correctly matched in each training image. However, it is impossible to achieve the ideal situation in the actual experimental process. First of all, the latitude and longitude ranges of the 50 training images are not exactly the same, they overlap with each other, and there are even cases of complete non-overlap, so it is impossible to match the features extracted from the reference image with all the 50 training images. Moreover, the number of filtered feature classes decreased with increasing \(\mathit{FMN}\). Since not all extracted feature points can be matched accurately, the matching process will miss the correct match, and it is not guaranteed that all similar feature points in the training image can be stored in a single feature class with the same feature label. In addition, the training image's features are impacted by the passage of time because it was taken at a different time than the one to which it belongs. There will undoubtedly be new features during the training image iteration, and feature points that cannot be successfully matched will be eliminated. Therefore, the number of feature classes with more matches is smaller, which also suggests that the feature class is more stable in this region range. Because different feature extraction algorithms exhibit different stability during iterative training of the feature database, the maximum \(\mathit{FMN}\) value is determined by the number of feature classes that can be filtered from the feature database within the test image region. The experiments in this section set a minimum threshold of 50 to ensure that a sufficient number of feature classes are extracted from the test image.