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A refined automatic co-registration method for high-resolution optical and sar images by maximizing mutual information

机译:通过最大化互信息来改进高分辨率光学和sar图像的自动共配准方法

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摘要

The use of multisensors images for different applications, like change detection and image fusion, require an image to image registration. Actually, the image registration becomes a crucial task with the continuous increase in image resolution, especially in urban area. In this paper, a refined automatic Mutual Information based registration approach of spaceborne Synthetic Aperture radar (SAR) and optical image is proposed. The data set include TerraSar-X (1m) and Worldview-2 (0.5m) images and cover urban area of San Francisco. Our approach is divided into two steps. First, we use Discrete Canny edge detector to extract contours from the optical image, and Gabor-Wavelet filter to extract edge from the SAR image. Second, a rigid transformation (rotation and translation) is applied to the contours image obtained by Canny detector. Then, for each value of transformation, the Mutual Information is computed between this transformed image and the feature image outcome of application of Gabor-wavelet to the SAR image. The best transformation parameters are obtained when the Mutual Information is maximal. Also, in this work, we compared the proposed approach with the basic Mutual Information intensity based registration method. The results obtained demonstrated that our approach improve the registration accuracy comparatively to the basic Mutual Information intensity based registration method.
机译:将多传感器图像用于变化检测和图像融合等不同应用需要图像到图像配准。实际上,随着图像分辨率的不断提高,图像配准已成为一项至关重要的任务,尤其是在城市地区。本文提出了一种完善的基于互信息的星载合成孔径雷达(SAR)和光学图像自动配准方法。数据集包括TerraSar-X(1m)和Worldview-2(0.5m)图像,覆盖旧金山市区。我们的方法分为两个步骤。首先,我们使用离散Canny边缘检测器从光学图像中提取轮廓,并使用Gabor-Wavelet滤波​​器从SAR图像中提取边缘。其次,将刚性变换(旋转和平移)应用于通过Canny检测器获得的轮廓图像。然后,对于每个变换值,在该变换图像与将Gabor小波应用于SAR图像的特征图像结果之间计算互信息。互信息最大时,可获得最佳转换参数。同样,在这项工作中,我们将建议的方法与基于互信息强度的基本注册方法进行了比较。获得的结果表明,与基于基本互信息强度的基本注册方法相比,我们的方法提高了注册准确性。

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