  Multi-source image registration is challenging due to intensity, rotation,
and scale differences among the images. Considering the characteristics and
differences of multi-source remote sensing images, a feature-based registration
algorithm named Multi-scale 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
multi-scale matching strategy. Comprehensive experiments on 17 multi-source
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.
