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

arXiv: https://arxiv.org/abs/2204.00260 · HTML: https://mlsanity.com/paper/2204.00260/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 45 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity](https://mlsanity.com/paper/2103.16871/) (2021)
- [Fast and Robust Matching for Multimodal Remote Sensing Image Registration](https://mlsanity.com/paper/1808.06194/) (2018)
- [Multi-scale PIIFD for Registration of Multi-source Remote Sensing Images](https://mlsanity.com/paper/2104.12572/) (2021)

## Built on by (most cited first, 3 of 3)

- [R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor](https://mlsanity.com/paper/2212.02277/) (2022)
- [3MOS: Multi-sources, Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching](https://mlsanity.com/paper/2404.00838/) (2024)
- [A Robust Multisource Remote Sensing Image Matching Method Utilizing Attention and Feature Enhancement Against Noise Interference](https://mlsanity.com/paper/2410.11848/) (2024)

## Abstract (arXiv)

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.