MS-HLMO: Multiscale Histogram of Local Main Orientation for Remote Sensing Image Registration

Multisource image registration is challenging due to intensity, rotation, and scale differences among the images. Considering the characteristics and differences in multisource remote sensing images, a feature-based registration algorithm named multiscale histogram of local main orientation (MS-HLMO) is proposed. Harris corner detection is first adopted to generate feature points. The HLMO feature of each Harris feature point is extracted on a partial main orientation map (PMOM) with a generalized gradient location and orientation histogram-like (GGLOH) feature descriptor, which provides high intensity, rotation, and scale invariance. The feature points are matched through a multiscale matching strategy. Comprehensive experiments on 17 multisource remote sensing scenes demonstrate that the proposed MS-HLMO and its simplified version MS-HLMO+ outperform other competitive registration algorithms in terms of effectiveness and generalization.