A Robust Multiscale Edge Detection Method for Accurate SAR Image Registration

Abstract

Edge detection is a technique used to identify inherent structures within an image, and it is an essential requirement for synthetic aperture radar (SAR) applications. In particular, ratio-based edge detectors have been widely used in SAR image registration because of their ability to extract invariant features and reduce the effects of speckle noise. However, current edge detectors often struggle to accurately detect multi-scale objects and low-contrast structures. To address this issue, we present a robust multi-scale edge detector that uses a modified convolution kernel to improve the extensibility of edge features and aggregates multi-scale feature responses. We also propose a local scale estimation module to enhance edge responses in low-contrast areas and reduce noise effects. The experimental results demonstrate that our proposed method effectively preserves the integrity, continuity, and robustness of multi-scale and low-contrast structures. By incorporating our proposed edge detector into feature and template matching frameworks, we are able to significantly improve matching accuracy and outperform state-of-the-art SAR image registration methods.