A Robust Strategy for Large-Size Optical and SAR Image Registration

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-07, Vol.14 (13), p.3012
• Main Authors: Li, Zeyi, Zhang, Haitao, Huang, Yihang, Li, Haifeng
• Format: Article
• Language: English
• Subjects: Affine transformations; Algorithms; Datasets; Feature selection; Image registration; Methods; nonlinear deformation; One-Class SVM; Outliers (statistics); Radiation; Registration; Robustness; Similarity; similarity map; Support vector machines; Synthetic aperture radar; synthetic aperture radar (SAR); Template matching
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14133012

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images.