A Novel Cross-range Scaling Algorithm for ISAR Images Based on Feature Registration

As Inverse SAR (ISAR) imaging utilizes synthetic aperture with aspects changes related to the Radar Line of Sight (RLOS) to acquire azimuth resolution, the accurate estimation of rotated velocity is pivotal for the geometric scaling of ISAR images to measure the real size of a target. Compared with current methods by estimating motional parameters and the integrated images registration, this paper proposes a novel algorithm by extracting and registering the interested points of ISAR images from sub-aperture data, which provides the points coordinate-locations to calculate the virtual rotated velocity. First, adequate interested points are extracted from two sub-aperture images by Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). Those points are then pinpointed by matching and re-matching with the minimum Euclid- distance and RANdom SAmple Consensus (RANSAC) principles, respectively. Finally, the rotated velocity, a premise to acquire the cross-resolution, can be estimated to achieve the precise target scaling. Simulated and real data validate the effectiveness and robustness of the proposed algorithm.