## 3 Results and Discussion  
In order to verify the effectiveness of the processing method, 21 obtained aerial infrared images were used for region stitching processing experiments. We have developed a fast processing program using VC using a high-performance server, including four main modules: an image geometric correction module based on rigorous models, extraction of homonymous points between adjacent images, a coarse stitching module based on SIFT for adjacent images, and a fine-stitching module for local matching. The specific experimental steps are as follows. The first step was each image's geometric correction based on rigorous models. The image geometry correction module based on a rigorous model utilizes the rigorous geometric relationship of multi-element parallel scanning imaging. Each image is obtained by a multi-element parallel scanning camera, which is obtained by scanning an exposure gap (corresponding to a linear array CCD) along the flight side. For each scanning line image obtained by a multi-element parallel scanning camera, its geometric relationship is equivalent to a tabletop array CCD camera tilting a scanning angle along the flight side \(\theta\). Based on the characteristics of centerline (\(y=0\)) imaging and according to the Collinearity equation model of photogrammetry, the Collinearity equation Formula (1), which was applicable to the acquisition of images by multiple parallel scanning cameras, is obtained. At the same time, in order to improve the speed of image geometric correction based on the Collinearity equation model of the multi-element parallel scanning camera, the block correction method was adopted for each infrared image. That is, the image is divided into several sub-areas and then separately corrected in each sub-area. The strict Collinearity equation model was used to calculate the coordinates of the four corners of each image to obtain the geographical coordinates of the corrected image. For each pixel point in each region, the perspective transformation model Formula (2) was used to calculate the coordinates of each pixel based on the coordinates of the four corner points. The second step was to extract points with the same name between adjacent images. The extraction module for homonymous points between adjacent two images utilizes the approximate geometric overlap relationship between the two images to extract the corresponding image blocks from the previous image and the next image. Then, SIFT feature points are separately extracted, and the best homonymous point pair is extracted  
Figure 8: Mosaic image by local fine matching. Figure 6: Two adjacent images. Figure 7: Mosaic image by manual mode. based on the ratio relationship between the nearest neighbor and the next-nearest neighbor, as well as a bidirectional comparison strategy. The third step was to perform coarse stitching of adjacent images based on SIFT. The coarse stitching module of adjacent images based on SIFT was achieved by extracting points with the same name through SIFT and calculating translation, scale, and rotation parameters. Based on SIFT, a pair of optimal homonymous point pairs were extracted on the left and right sides of the image, and these two points were used as controls. The main geometric deformations of adjacent images were translation, scale, and rotation. The required translation, scale, and rotation parameters were obtained using Formula (3), and then the calculated scale, scale, and rotation parameters were used to resample the current image using Formula (4), and then we could obtain an image that was basically consistent with the previous position. The fourth step was fine stitching of local matching. The precise stitching module for local matching used a normalized cross-correlation matching algorithm to obtain the coordinates of feature points with the same name in the overlapping area of two adjacent images. It adjusts the scanning direction (column direction) of the current image.