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arXiv 2204.002602022-04-01eess.IVcs.CV

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

Beijing Institute of Technology（北京理工大学）；Mississippi State University（密西西比州立大学）

Chenzhong Gao, Wei Li, Ran Tao, Qian Du

DOI：: 10.1109/TGRS.2022.3193109

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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.

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