直方图
人工智能
方向(向量空间)
特征(语言学)
图像配准
计算机科学
计算机视觉
旋转(数学)
模式识别(心理学)
特征提取
比例(比率)
匹配(统计)
直方图匹配
遥感
图像(数学)
数学
地理
几何学
统计
哲学
地图学
语言学
作者
Chenzhong Gao,Wei Li,Ran Tao,Qian Du
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-14
被引量:10
标识
DOI:10.1109/tgrs.2022.3193109
摘要
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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