# A Method of Aerial Multi-Modal Image Registration
## 1 Introduction  
The approach and landing phases of an aircraft are important parts of their safe flight [1; 2; 3]. In most cases, visual observation by the pilot in command is used for landing, so this is a great potential safety hazard. This phase is also an important phase for flight accidents. If we can provide captains with timely information on the flight situation and scenes outside the window during approaching and landing, to enhance captains' vision. Meanwhile, we can help them to correctly perceive and operate, as well as greatly reduce the incidence of flight accidents in the aircraft approach and landing phases. The fusion of multi-modal images, such as infrared images and visible-light images, can provide more visual information for the captain. The premise of image fusion is to correctly realize image registration. Traditional feature extraction algorithms, such as the SIFT (Scale-Invariant Feature Transform) [4; 5; 6], SURF (Speeded-Up Robust Feature) [7; 8; 9], ORB (Oriented FAST and Rotated Brief) [10; 11], and FAST (Features from Accelerated Segment Test) [12; 13] operators, are not prominent for visible images under low-visibility conditions due to the blurring of edges and detailed information. [PERSON] et al. [14] proposed a method that used a morphological gradient and C\({}_{-}\) SIFT, which was a new algorithm for real-time adaptive registration of visible-light and infrared images. The algorithm could quickly complete the registration of visible-light and infrared images, and the registration accuracy was satisfactory. [PERSON] et al. [15] proposed a novel keypoint detector to extractcorners and spots at the same time. The proposed detector was named Harris-Difference of Gaussian (DoG), and it combined the advantages of the Harris-Laplace corner detector and DoG blob detector. The experimental results showed that Harris-DoG could effectively increase the number of correct matches and improve the registration accuracy compared with the most advanced keypoint detector. [PERSON] et al. [16] proposed a multi-modal remote sensing image registration method that had scale, radiation, and rotation invariance. These studies integrated several methods of feature extraction to achieve better performance than that obtained with the extraction of a single feature. It was found in a large number of experiments that for infrared and visible-light images with low visibility, the aforementioned features do not describe the image pairs to be registered well. With the popularity of deep learning and neural networks, scholars have begun to focus on multi-modal image registration based on neural networks [17; 18; 19; 20; 21]. [PERSON] et al. [22] achieved the registration of multi-modal images through a space conversion network and translation network. They trained the translation network from image to image on two input modes, bypassing the difficulty of developing a cross-mode similarity measurement. [PERSON] et al. [23] proposed an RFNet network structure to achieve multi-modal image registration and fusion in the framework of mutual enhancement, and this processed registration from coarse to fine. This method used the feedback of image fusion to improve the registration accuracy for the first time, instead of treating these as two independent problems. Fine registration also improved the fusion performance. [PERSON] et al. [24] proposed a depth feature correlation learning network (Cnet) for multi-modal remote sensing image registration to solve the problem of the existing depth learning descriptor not being suitable for multi-modal remote sensing image registration. In view of the differences between infrared and visible-light images in terms of resolution, spectrum, angle of view, etc., deep learning was used to integrate image registration, image fusion, and semantic requirements into a framework, and an automatic registration algorithm that corrected the geometric distortion in the input image under the supervision of photometric and endpoint constraints was proposed [25; 26; 27].