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Automatic Coregistration of SAR and Optical Images Exploiting Complementary Geometry and Mutual Information

机译:SAR和光学图像的自动配准,利用互补的几何和互信息

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Image coregistration aims at stacking two or multiple images in a way such that, for each image, the same pixel corresponds to the same point of the target scene (possibly with sub-pixel accuracy). We can distinguish two families of image coregistration problems, basically depending on if the images to be coregistered are taken by sensors of the same or different type (e.g., sensing different wavelenghts), and with similar or different illumination and acquisition geometries (e.g. different sun illumination conditions and/or different acquisition incidence angles). Whilst the first type of image coregistration is well established, multimodal coregistration is not yet well founded and due to difficulty of finding correspondences between the images (tie points) in a robust way, and the avable approaches often recur to manual assistance. The multimodal image coregistration technique proposed in this work overcomes the problems due to differences in radiometries and in geometries by exploiting two main concepts: complementary geometry information between the images to be coregistered, and mutual information (or entropy) as similarity metric. The method focuses on coregistration of very high resolution synthetic aperture radar (SAR) and optical images, but the approach is of general validity. The tests performed on real very high resolution optical and SAR data confirm the validity of the method.
机译:图像配准旨在以一种方式堆叠两个或多个图像,以便对于每个图像,相同像素对应于目标场景的相同点(可能具有亚像素精度)。我们可以区分两个系列的图像配准问题,基本上取决于要共配准的图像是由相同或不同类型的传感器(例如,感测不同的波长)以及具有相似或不同的照明和采集几何结构(例如,不同的太阳)拍摄的照明条件和/或不同的采集入射角)。虽然已经很好地建立了第一类型的图像融合,但由于很难以健壮的方式找到图像之间的对应关系(联系点),因此多模态融合还没有很好的基础,并且可用的方法通常依赖于人工协助。在这项工作中提出的多峰图像融合技术通过利用两个主要概念克服了由于放射线学和几何学上的差异而产生的问题:要共同配准的图像之间的互补几何信息,以及作为相似性度量的互信息(或熵)。该方法侧重于非常高分辨率合成孔径雷达(SAR)和光学图像的配准,但是这种方法具有普遍的有效性。对真正的非常高分辨率的光学和SAR数据进行的测试证实了该方法的有效性。

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