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.
@article{arxiv.2204.00260,
title = {MS-HLMO: Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration},
author = {Chenzhong Gao and Wei Li and Ran Tao and Qian Du},
journal= {arXiv preprint arXiv:2204.00260},
year = {2022}
}