# 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
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & \begin{tabular}{c} **Detector** \\ **Type** \\ \end{tabular} & \multicolumn{2}{c}{**Description**} \\ \hline SIFT & Blobs & \begin{tabular}{c} SIFT first constructs the Difference-of-Gaussian scale space based on the Gaussian scale space; next, the keypoints are detected and precisely located in the Difference-of-Gaussian images; after that, one or more orientations are determined by the peak of the gradient histogram of each key point neighborhood. Finally, 128-dimensional feature descriptors are constructed based on the gradient information of the neighborhood centered on the keypoints. \\ \hline SURF & Blobs & \begin{tabular}{c} SURF uses box filters to convolve with the original image to construct the scale space while using the integral image technique to increase the computational efficiency of the algorithm. It detects the candidate feature points using the Hessian Matrix, followed by non-maximal suppression. To determine the keypoint orientation, SURF adds up the Haar-wavelet responses in the horizontal and vertical orientation of the \(\frac{\pi}{3}\) angular sector sliding window in the circular neighborhood of the keypoint. The orientation of the longest such vector sum is used as the keypoint orientation. Furthermore, SURF statistics the Haar-wavelet responses in the area around the keypoint to create a 64-dimensional feature descriptor. \\ \hline KAZE & Blobs & \begin{tabular}{c} KAZE uses efficient Additive Operator Splitting (AOS) techniques for nonlinear diffusion filtering to build nonlinear scale space, which reduces noise while maintaining edges. It searches for Hessian local maxima on the nonlinear scale space after normalization at different scales as the keypoint and finds the dominant orientations of feature points in a similar way to SURF. KAZE builds the descriptor using a variant of the SURF descriptor, Modified-SURF (M-SURF) [30], which can handle the boundaries better than the original SURF descriptor and finally forms a 64-dimensional feature vector. \\ \hline AKAZE & Blobs & \begin{tabular}{c} AKAZE is an accelerated variant of KAZE. It constructs nonlinear scale spaces more quickly by using the Fast Explicit Diffusion (FED) mathematical framework. Similar to KAZE, It locates candidate points and filters them at each octave to perform keypoint extraction. It calculates the dominant orientations of keypoints in a similar way to KAZE. It uses an updated Modified-Local Difference Binary (M-LDB) binary descriptor for descriptor construction that not only compares region means instead of individual pixels in the binary set but also incorporates rotation invariance. \\ \hline ORB & Corners & \
\begin{tabular}{c} ORB consists of a modified FAST (Features from Accelerated Segment Test) [31] and a rotated BRIEF (Binary Robust Independent Elementary Features) [32]. It first uses FAST to quickly select keypoints; afterward, it uses the intensity centroid method to calculate keypoint orientations; and finally, it uses the modified BRIEF to create binary descriptors, compared with the original BRIEF descriptors with increased rotational invariance compared with the original BRIEF. \\ \hline FREAK & Corners &
\begin{tabular}{c} Only the descriptor extraction approach is improved by FREAK. The keypoint detection algorithm in BRISK is used in the original paper to perform FAST feature point detection on the constructed multi-scale space. However, unlike the uniform sampling pattern of BRISK for extracting feature descriptors, FREAK is inspired by the human visual system and uses a retinal sampling pattern where the smaller the distance from the keypoint, the denser the sampling, and the larger the distance from the keypoint, the more discrete the sampling points. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief introduction of multiple local invariant feature algorithms.