# 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 feature points successfully matched in \(F_{k}\): The matching parameters of the feature \(j\) in \(F_{k}\) are updated directly, the number of matches is increased by one, \(M_{j}=M_{j}+1\), the number of consecutive matches is increased by one, \(CM_{j}=CM_{j}+1\), the number of unmatched matches remains unchanged, \(UM_{j}=UM_{j}\), and the number of consecutive unmatched matches is reduced to zero, \(CUM_{j}=0\). * Feature points in \(F_{k}\) that failed to match: The matching parameters are updated according to the label of the unmatched feature \(j\) in \(F_{k}\). If there is a successful matching point with the same label \(Index\) as the feature \(j\), it means that the feature class of the feature \(j\) is successfully matched, but the feature \(j\) is not successfully matched; at this time, the number of matches is increased by one, \(M_{j}=M_{j}+1\), the number of consecutive matches is increased by one, \(CM_{j}=CM_{j}+1\), the number of unmatched points is increased by one, \(UM_{j}=UM_{j}+1\), and the number of consecutive unmatched points is increased by one, \(UM_{j}=UM_{j}+1\), and the number of consecutive unmatched points is increased by one, \(CUM_{j}=CUM_{j}+1\). If there is no successful matching point with the same label, the number of matches remains unchanged, \(M_{j}=M_{j}\), the number of consecutive matches is cleared, \(CM_{j}=0\), the number of unmatched matches increases by one, \(UM_{j}=UM_{j}+1\), and the number of consecutive unmatched matches is increased by one, \(CUM_{j}=CUM_{j}+1\). * The feature points successfully matched in \(T_{k}\): In \(T_{k}\), the matched feature \(j\) belongs to a feature class of \(F_{k}\). After correcting the position according to the matching result, the feature \(j\) is added to \(F_{k}\) with the same \(Index\), \(M_{j}\) and \(CM_{j}\) changed to the same as the matched features in \(F_{k}\), with zero unmatched, \(UM_{j}=0\) and zero consecutive unmatched, \(CUM_{j}=0\). * Feature points in \(T_{k}\) that failed to match: The unmatched feature \(j\) in \(T_{k}\), after correcting the position according to the matching result, will also be added to the feature database set with a new \(Index\) for the newly added feature, the number of matches and consecutive matches is 0, \(M_{j}=CM_{j}=0\), and the number of unmatched and consecutive unmatched is 1, \(UM_{j}=CUM_{j}=1\), which corresponds to the newly added feature points. * For other features that are not in the extracted feature set \(F_{k}\) but exist in the feature database, they remain unchanged in the feature database. * Delete feature points within the feature set based on the threshold: After the above steps, the features are filtered for \(F_{k}\), and when the proportion of unmatched times between them to the total number of all training at that point exceeds a threshold, \(\frac{UM_{j}}{M_{j}+UM_{j}}>\mathit{thre1}\), or when the number of consecutive unmatched times exceeds a threshold, \(\mathit{CUM_{j}}>\mathit{thre2}\), the features are removed from the feature set \(F_{k}\). In this paper, we empirically set the parameters \(\mathit{thre1}=0.5\) and \(\mathit{thre2}=4\) to rewrite the filtered feature set \(F_{k}\) into the feature database. 2. Repeat the above process to iteratively train the feature images: The feature database is continuously updated and iterated using training images, in which feature points that can be matched multiple times are automatically clustered to obtain stable feature classes with the same label, which is equivalent to aggregating multiple feature descriptors at the same location under multiple observation conditions, thus realizing the training process of the feature database. The feature class is a cluster of descriptors of the same feature point location under different observation conditions. For the same feature class, the number of matches is identical. In this case, the matching number of a feature class represents to some extent the stability of the feature class.