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

中文翻译：

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MS-HLMO：遥感图像配准局部主方向多尺度直方图


由于图像之间的强度、旋转和比例差异，多源图像配准具有挑战性。针对多源遥感图像的特点和差异，提出一种基于特征的局部主方向多尺度直方图（MS-HLMO）配准算法。首先采用Harris角点检测来生成特征点。每个 Harris 特征点的 HLMO 特征是在部分主方向图 (PMOM) 上提取的，具有广义梯度位置和方向直方图 (GGLOH) 特征描述符，该特征描述符提供高强度、旋转和尺度不变性。通过多尺度匹配策略来匹配特征点。对 17 个多源遥感场景的综合实验表明，所提出的 MS-HLMO 及其简化版本 MS-HLMO+ 在有效性和泛化性方面优于其他竞争配准算法。