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Multisensor remote sensing registration method and system based on dense feature of orientated phase

  • role： First author第一作者
  • Affiliation：

    Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China

  • Email：yeyuanxin110@163.com
  • Introduction：叶沅鑫，研究方向为遥感图像处理与分析。E-mail： yeyuanxin110@163.com
YE Yuanxin,  
  • Affiliation：

    Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China

WANG Mengmeng,  
  • Affiliation：

    Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China

YANG Chao,  
  • Affiliation：

    Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China

YU Zhirui,  
  • Affiliation：

    Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China

GE Xuming

resumen

To solve the problem of registration difficulty caused by considerable geometric distortion and gray differences between multisensor remote sensing images, this study proposes a fast and accurate registration method based on structural similarity between images. In this method, the phase congruency model with illumination and contrast invariances is introduced to construct robust structural feature descriptors of images. First, the intensity and orientation of phase congruency are used to build a pixel-wise three-dimensional structural feature representation named Dense Feature of Orientated Phase (DFOP), which can effectively resist the grayscale difference between multisensor 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 fast matching with subpixel accuracy by employing a template matching scheme. In addition, a fast and robust automatic multisensor remote sensing image registration system is developed on the basis of the proposed DFOP. Finally, the proposed method and registration system is validated using multiple pairs of multisensor remote sensing images (including optical, LIDAR, and SAR) covering different scenes. 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

palabra clave

multi-sensor remote sensing images;image registration;phase congruency;dense feature of orientated phase;images registration system

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