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引用本文:

DOI:

10.11834/jrs.20221765

收稿日期:

2021-11-25

修改日期:

2022-02-23

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基于方向相位稠密特征的多传感器遥感影像配准方法和系统(首届《遥感学报》青年学术论坛)
王蒙蒙, 叶沅鑫, 杨超, 喻智睿, 葛旭明
西南交通大学
摘要:

针对多传感器遥感影像间显著几何畸变和灰度差异造成的配准困难问题,本文提出了一种基于结构相似性的快速精确配准方法。为了构建多传感器影像间的稳健结构特征,引入光照和对比度不变性的相位一致性模型,利用相位一致性特征值和特征方向构建了一种逐像素的三维结构特征描述符—方向相位稠密特征(Dense Feature of Orientated Phase, DFOP),该特征描述符通过捕捉影像的几何结构分布,能够有效抵抗多传感器影像间的灰度差异。然后,基于模板匹配的策略,将DFOP描述符变换到频率域,并进行快速子像素精度匹配。此外,在DFOP的基础上,本文研制了一种快速鲁棒的多传感器遥感影像配准系统。通过利用多种地貌类型的多传感器遥感影像对所提出的方法进行测试,实验结果表明,相较于其它基于灰度或结构特征的匹配方法,DFOP能够获得更高的匹配正确率,且所开发的配准系统(下载链接 )也优于商业软件ENVI以及ERDAS中配准模块的配准精度。

A Multi-sensor remote sensing registration method and system based on dense feature of orientated phase
Abstract:

To solve the problem of registration difficulty caused by significant geometric distortion and gray differences between multi-sensor remote sensing images, this paper proposes a fast and accuracy registration method based on structural similarity between images, where the phase congruency model with illumination and contrast invariances is introduced to construct the robust structural features descriptors of images. Firstly, the intensity and orientation of phase congruency are used to build a pixel-wise three-dimensional structural feature representation named DFOP (Dense Feature of Orientated Phase), which can effectively resist the grayscale difference between multi-sensor images by capturing geometric structures of images. Next, the DFOP feature descriptor is transformed into the frequency domain, and the single-step DFT approach is used to achieve the fast matching with a subpixel accuracy by employing a template matching scheme. In addition, we also developed a fast and robust automatic multi-sensor remote sensing image registration system based on proposed DFOP. Finally, the proposed method and registration system has been validated using multiple pairs of multi-sensor remote sensing images (including optical, LIDAR and SAR) covering different scenes, the results show that the proposed DFOP achieves higher correct matching rate, and the developed registration system outperforms the registration module of ENVI and ERDAS in registration accuracy. Our system is available at https://github.com/yeyuanxin110/Remote-Sensing-Image-Registration-system.git

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