Given the imaging characteristics of synthetic aperture radar (SAR) images and the inherent speckle noise in them, scale-invariant feature transform based algorithms are unable to perform satisfactorily.To improve registration efficiency between SAR images, 2010 chevy malibu exhaust manifold we propose a robust and efficient registration method with three main contributions.First, considering sudden dark patches appearing in SAR images, we propose the ratio of exponentially weighted average blocks to suppress the sudden dark patches and better adapt to different test images.This new operator called blocks of the ratio of exponentially weighted averages (ROEWA-B) divides the processing windows of ROEWA into blocks, which can not only reduce speckle noise but also retain more edge details compared to ROEWA when sudden dark patches appear.Second, for outlier removal, we present an approach using the jetstream ice therapy parts minimum moment map to remove erroneous keypoints.
Finally, based on the gradient location orientation histogram descriptor, we propose a novel multiscale circle descriptor, which combines scale change information to give weights to feature points at different scales.Experimental results for various thresholds and evaluations demonstrate the advantage and robustness of our method in registration.