MS-HLMO: Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration
Chenzhong Gao, Wei Li, Ran Tao, Qian Du

TL;DR
This paper introduces MS-HLMO, a novel feature-based algorithm for remote sensing image registration that effectively handles intensity, rotation, and scale differences by using multi-scale histograms of local orientations.
Contribution
The paper proposes MS-HLMO, a new multi-scale feature descriptor and matching strategy tailored for multi-source remote sensing image registration, demonstrating superior performance.
Findings
MS-HLMO outperforms existing algorithms in effectiveness.
MS-HLMO shows strong generalization across diverse scenes.
The simplified MS-HLMO+ maintains high accuracy.
Abstract
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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization · Satellite Image Processing and Photogrammetry
