# A Method of Aerial Multi-Modal Image Registration
## 4 Experimental Analysis  
The effect of infrared thermal imaging image sequence, visible image sequence and image sequence after registration fusion collected during the experiment is shown in Figure 5. It is worth noting that the parameters used in the experimental results are \(\sigma=\sqrt{2},N=64,\eta=0.7,r=0.4\). The single-step execution result is shown in Figure 6. In the first row, they are infrared and visible source images, respectively. It is not difficult to see that the visible image is affected by low visibility and the details are blurred, which brings difficulties to image matching. Therefore, as shown in Figure 6c,d, more curvature corners are extracted from infrared images than visible images. For contour corners, the histogram of gradient amplitude in the range of 0 to 180 degrees is calculated by analogy with the traditional SIFT operator, and a 128-dimension feature descriptor is generated to realize rough matching. In coarse matching (see Figure 6e), error matching often occurs in the sky area of the infrared image. In fine matching, 16 pairs of point-to-point matching are randomly selected, as shown in Figure 6f, and there is no matching error. Figure 5: Multi source image registration and fusion effect at different stages of approach and landing. Registration Quality Evaluation  
We used two registration quality measurements(RQM) to assess the accuracy of registration, namely subjective and objective RQM. In subjective evaluation, pixel average fusion and checkerboard presentation are adopted. In the checkerboard presentation, the  
Figure 6: The single-step execution result. infrared image and the registered visible image are segmented in the same way, and either of them is spliced into an image at the same location, as shown in Figure 7b. The fusion result based on the average pixel value of the infrared image and visible image is shown in Figure 7. In order to more intuitively explain the accuracy of registration, in the checkerboard fusion results, the segmented areas of roads and fields in the infrared image are completely collinear with the adjacent visible image, and the registration results are consistent with the naked eye judgment. Figure 7: \(Cont.\)  
The subjective evaluation of the fused image quality depends on the human visual perception system, focusing on image details, contrast, definition and other spectral characteristics, but the image quality is also affected by other aspects except for spectral characteristics. The human visual perception system is not sensitive to these aspects, so the subjective evaluation is not comprehensive. Therefore, researchers have proposed some objective evaluation methods that can be measured quantitatively based on information theory, structural similarity, image gradient and statistics. The following three quality evaluation indicators are mainly used to evaluate the quality of fused images in this paper:  
1. Root Mean Square Error: Because the real transformation parameters between images are not known, the root mean square error \(RMSE\) between the matching point pairs involved in the transformation model calculation in image registration is used as the registration accuracy index. \[RMSE=\sqrt{\frac{\sum_{i=1}^{m}\|x_{1i}-Mx_{2i}\|^{2}}{n}}\] (18) where \(M\) is the transformation matrix between image \(I_{1}\) and \(I_{2}\), \(x_{1i}\) and \(x_{2}\) are the coordinates of matching point pairs in the overlapping area of adjacent images, and m is the number of matching point pairs in the overlapping area. By randomly extracting 10 frames of synchronous data from the video, \(RMSE~{}=~{}0.522\) is calculated and analyzed, and the geometric error of registration accuracy is within 1 pixel. 2. Entropy: Entropy is an indicator used to represent the amount of information in information theory, which can be used to measure the amount of information in fused images.