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.

Remote consultation image stitching method based on wireless sensor technology and mathematical morphology

“…This method is used to test the stitching of the patient's remote consultation image. The method based on convolutional neural network in reference [6] and single stress matrix compensation in reference [7]. The results are as follows.…”

“…The method in this paper can not only reduce the visual field of the retina without the seam of Figure 4, but also get an excellent visual effect on the basis of image stitching. In order to further verify the excellent effect of the method in this paper on image stitching, the left eye image is taken as an example, the method based on convolution neural network in reference [6] and the method based on homography matrix compensation in reference [7] are taken as the comparison methods, and the two comparison methods are used to splice the images respectively. The results are shown in Figure 6.…”

“…By solving the optimization problem in the sense of 0 l norm shown in formula (7), the exact or approximate approximation x of x can be obtained, that is: ˆxa  =…”

“…In formula (7): 8) is a convex optimization problem, it can be easily transformed into a linear programming problem, which can be solved by interior point method, gradient projection method, second-order cone programming, matching pursuit method and so on. The basic property of linear programming is to maximize or minimize the objective function under some constraints.…”

“…However, the calculation speed of this method is relatively slow, and it can not deal with large-scale image stitching ideally. Tian et al studied a new method of spaceborne scanning SAR image stitching based on homography matrix compensation [7], and preprocessed the image through an improved Wallis filter to eliminate intensity inconsistency. The overlapping region of adjacent images was roughly extracted by geographic location technology.…”