# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
###### Abstract  
Accurate and efficient image mosaicking is essential for generating wide-range swath images of spaceborne scanning synthetic aperture radar (ScanSAR). However, the existing methods cannot guarantee the accuracy and efficiency of stitching simultaneously, especially when mosaicking multiple large-area images. In this paper, we propose a novel image mosaic method based on homography matrix compensation to solve the mentioned problem. A set of spaceborne ScanSAR images from the Gaofen-3 (GF-3) satellite were selected to test the performance of the new method. First, images are preprocessed by an improved Wallis filter to eliminate intensity inconsistencies. Then, to reduce the enormous computational redundancy of registration, the overlapping areas of adjacent images are coarsely extracted using geolocation technologies. Furthermore, to improve the efficiency of stitching and maintain the original information and resolution of images, we deduce a compensation of homography matrix to implement downsampled images registration and original-size images projection. After stitching, the transitions at the edges of the images were smooth and seamless, the information and resolution of the original images were preserved successfully, and the efficiency of the mosaic was improved by approximately one thousand-fold. The validity, high efficiency and reliability of the method are verified. scanning synthetic aperture radar (ScanSAR); image mosaic; overlapping area extraction; homography matrix compensation; parallel registration; Gaofen-3 satellite 2021 2021 19 July 2021 22 July 2 smooth SAR images. They also suggested skipping the first octave of the scale-space pyramid to reduce the number of incorrect keypoint detections. [PERSON] et al. [17] used a multilook preprocessing method to suppress speckle noise. This method reduces the computational complexity at the expense of losing image features and reducing the resolution of the original image. To enhance the matching performance of SAR images, many studies have focused on improving the scale space. Bilateral filter SIFT (BFSIFT), adapted anisotropic Gaussian SIFT (AAG-SIFT), nonlinear diffusion scale space SIFT (NDSS-SIFT), and the SIFT-like algorithm for SAR images (SAR-SIFT) were proposed by replacing a Gaussian filter with several anisotropic filters to construct the scale space [18; 19; 20; 21]. This kind of method retains more image details and edges than other methods so that more keypoints can be obtained. However, keypoint detection and matching initially take up a large part of the time in the entire mosaic process, and these methods will increase the computational complexity. Another type of research optimized the image registration process, which consists of coarse registration and fine registration. [PERSON] et al. [22] proposed a novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information. First, SIFT is used for preregistration. Then, a fine-tuning process is implemented by maximizing the mutual information. [PERSON] et al. [23] proposed an automatic and novel SAR image registration algorithm. They downsampled images to reduce the computational complexity of SAR-SIFT. Although these methods have achieved a good mosaic effect on SAR images, there are still some limitations when they are applied to spaceborne ScanSAR image mosaics:  
1. The stitching seams cannot be eliminated completely in some special cases. After stitching, there will be inconsistent intensity distributions on different sides of the seam, which causes difficulties in the applications of large-area ScanSAR images. For example, if there is a ship just across the seam of the two stitched images, the inconsistent intensity distribution may lead to the failure of ship recognition. 2. Keypoint redundancy. Keypoint detection and matching will directly determine the accuracy and efficiency of stitching.