# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
###### Abstract
Most of the above algorithms are used to detect keypoints from the whole image by the SIFT algorithm, but the keypoints that can be matched correctly are from overlapping areas between adjacent images. For spaceborne ScanSAR, the overlapping areas between adjacent images generally account for 5% \(\sim\) 45% of the total image, which depends on the antenna angle, imaging geometry and latitude. Therefore, keypoint detection of the whole ScanSAR image will acquire a considerable number of redundant keypoints from nonoverlapping areas, resulting in much computational redundancy and stitching time. The redundant keypoints are not only unhelpful for keypoint matching but also reduce the matching accuracy due to some similar mismatched keypoints detected from nonoverlapping areas. [PERSON] et al. [24] proposed to convert the large-size small-overlap image registration into the small-overlap image registration by using the location of an airborne SAR image. This idea could also be used in spaceborne ScanSAR images. 3. Resolution reduction. The resolution of ScanSAR images is not as good as that of stripmap SAR or spotlight SAR because ScanSAR can obtain a wide-range swath at the expense of the azimuth resolution [25]. It will worsen by either multilooking or downsampling. In that case, many details and edges will be lost, and the image quality will be affected. 4. The mosaicking of multiple spaceborne ScanSAR images is a time-consuming process that is intolerable in practice. The swath of GF-3 ScanSAR varies from 300 km to 650 km, and an image of a single subswath has hundreds of millions of pixels. The computation will be enormous when mosaicking of multiple spaceborne ScanSAR images is required. Too many pixels will drastically slow down the detection and matching speeds, especially when using algorithms that increase the number of keypoints after optimization. Another type of method which should be discussed in this article is geolocation technology. It seems that the stitching of ScanSAR images can be realized by geolocation, which is fast. However, due to various errors, such as satellite measurement error, time error of SAR system, pulse repeat frequency error and relative height error, the geolocation of SAR processors cannot achieve sufficient accuracy in practice. As for the GF-3 satellite, the accuracy of a location is 230 m, and the resolution of narrow scanning (NS) is 50 m [11]. It can be seen that using geolocation can only guarantee the efficiency of stitching instead of the accuracy. To improve the accuracy of geolocation, the SAR processors should be more complex, which is very difficult. Thus, the traditional feature-based mosaic method can only guarantee the accuracy rather than efficiency of stitching, while the geolocation-based mosaic method can only guarantee the efficiency rather than accuracy of stitching. To solve the problems mentioned above, we propose a novel mosaic method for spaceborne ScanSAR images. First, an improved Wallis filter is used to eliminate the inconsistency of intensity and contrast between adjacent images before mosaicking. Then, the overlapping areas of images are coarsely extracted by geolocation technologies to remove the redundant areas of images that are useless for registration. In the image registration process, we reduce the size of the extracted overlapping areas and divide them into equal parts perpendicular to the stitching direction so that keypoint detection by the SIFT algorithm and keypoint matching can run in parallel. The homography matrix is calculated by matched keypoints, filtered by the random sample consensus (RANSAC) algorithm and compensated based on the multiple of overlapping area downsampling. After scaling compensation, the matrix can be used to project the original-size images so that the original resolution can be preserved. To validate the performance of this method, we selected a group of ScanSAR image data acquired by the GF-3 satellite for experiments.