HOPC: A NOVEL SIMILARITY METRIC BASED ON GEOMETRIC STRUCTURAL PROPERTIES FOR MULTI-MODAL REMOTE SENSING IMAGE MATCHING

Abstract. Automatic matching of multi-modal remote sensing images (e.g., optical, LiDAR, SAR and maps) remains a challenging task in remote sensing image analysis due to significant non-linear radiometric differences between these images. This paper addresses this problem and proposes a novel similarity metric for multi-modal matching using geometric structural properties of images. We first extend the phase congruency model with illumination and contrast invariance, and then use the extended model to build a dense descriptor called the Histogram of Orientated Phase Congruency (HOPC) that captures geometric structure or shape features of images. Finally, HOPC is integrated as the similarity metric to detect tie-points between images by designing a fast template matching scheme. This novel metric aims to represent geometric structural similarities between multi-modal remote sensing datasets and is robust against significant non-linear radiometric changes. HOPC has been evaluated with a variety of multi-modal images including optical, LiDAR, SAR and map data. Experimental results show its superiority to the recent state-of-the-art similarity metrics (e.g., NCC, MI, etc.), and demonstrate its improved matching performance.