A Morpho-Phase Feature-Based Method for Geometric Error Mitigation in InSAR Image Matching

Interferometric Synthetic Aperture Radar (InSAR) is a promising payload for Unmanned Aerial Vehicle (UAV) scene matching navigation due to the rich textures in interferogram images compared to SAR intensity images. However, geometric parameter estimation errors during reference interferogram image generation cause significant textural discrepancies with real-time data. Compounded by inherent non-local similarity of InSAR images, these issues render conventional matching algorithms ineffective, degrading navigation accuracy. To address these challenges, this paper proposes a Morpho-Phase feature-based InSAR image matching method to mitigate the impact of parameter errors. Firstly, a Phase-Robust Keypoint (PRK) detection method is proposed, which overcomes the impact of parameter errors on keypoint detection by introducing a compensated phase and extracting phase extrema. Secondly, a Hierarchical Morphological-Phase Descriptor (HMPD) is constructed to resolve the feature ambiguity caused by the non-local similarity of interferograms by combining morphological features with phase statistics. Experimental results based on real-world InSAR data demonstrate that the proposed matching method effectively mitigates the impact of parameter errors on InSAR image matching, enhances navigation positioning accuracy, and provides stable, high-precision positioning capabilities in practical scene matching navigation tasks.