Adaptive Registration for Optical and SAR Images With a Scale-Constrained Matching Method

Abstract

Images registration for optical and synthetic aperture radar (SAR) is a key of multi-sensor images analysis. And parameters selection affects the final result of the registration algorithms for optical and SAR images. For images obtained by different sensors, how to choose an appropriate parameter for accurate registration is a key problem. In this letter, a parameter adaptive registration algorithm between optical and SAR images based on SIFT is proposed. Because of the different imaging mechanisms of these two kinds of images, the algorithm uses the multiscale Sobel operator to calculate the gradient for the optical image, while for the SAR image, a new adaptive operator based on the neighborhood pixel value is proposed to calculate the gradient. In the feature extraction, the adaptive value estimated by constant false alarm rate (CFAR) detection is used instead of the fixed threshold. Finally, a matching method constrained by image size scaling (Scale-Constrained Fast Sample Consensus) is proposed. The evaluation was designed in two aspects: feature extraction and image registration. The algorithm we proposed shows excellent performances in the aspects of repeatability, correct matching rate and root mean square error. The experimental results show that our method maintains the performance of the original algorithm and has some optimization and breakthrough.