Abstract
Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to the influence of SAR speckle noise and nonlinear radiometric differences. This study proposes a robust algorithm based on phase congruency to register optical and SAR images (ROS-PC). It consists of a uniform Harris feature detection method based on multi-moment of the phase congruency map (UMPC-Harris) and a local feature descriptor based on the histogram of phase congruency orientation on multi-scale max amplitude index maps (HOSMI). The UMPC-Harris detects corners and edge points based on a voting strategy, the multi-moment of phase congruency maps, and an overlapping block strategy, which is used to detect stable and uniformly distributed keypoints. Subsequently, HOSMI is derived for a keypoint by utilizing the histogram of phase congruency orientation on multi-scale max amplitude index maps, which effectively increases the discriminability and robustness of the final descriptor. Finally, experimental results obtained using simulated images show that the UMPC-Harris detector has a superior repeatability rate. The image registration results obtained on test images show that the ROS-PC is robust against SAR speckle noise and nonlinear radiometric differences. The ROS-PC can tolerate some rotational and scale changes.
Highlights
The rapid development of sensor technology provided multiple remote sensing images for the observation of the Earth
We address the above limitations by developing a robust optical and synthetic aperture radar (SAR) image registration method based on phase congruency (PC) (ROS-PC)
Experiments are conducted to evaluate the tolerance of rotation and scale changes from the ROS-PC method
Summary
Feature-based methods are recommended for optical and SAR image registration because they process images with their significant features rather than all intensity information, thereby achieving high precision and robustness to geometry and radiation differences. Numerous methods have achieved improvements in gradient redefinition and descriptor construction when encountering optical and SAR images with large nonlinear radiation differences, the matching performance of feature descriptors based on gradient information is not ideal, and there are still many mismatches. A dense descriptor named the histograms of oriented magnitude and phase congruency was proposed to register multi-sensor images It is based on the combination of the magnitude and PC information of local regions, and successfully captures the common features of images with nonlinear radiation changes [37]. The HOSMI feature description method is proposed based on the histograms of phase congruency orientation on multi-scale max amplitude index maps.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
