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Application of multiresolution wavelet pyramids and gradient search based on mutual information to sub-pixel registration of multisensor satellite imagery

机译:基于互信息的多分辨率小波金字塔和梯度搜索在多传感器卫星图像亚像素配准中的应用

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Accurate geometric registration is an important step that precedes various tasks of processing of remotely sensed imagery. Assuming that, after radiometric and systematic correction, images are registered to within a few pixels, our goal is to develop fast and reliable automatic registration methods for multi-sensor data that would yield sub-pixel accuracy. This paper compares two gradient-based algorithms for sub-pixel image registration developed by Thevenaz et al. One of them optimizes intensity difference while the other maximizes mutual information between two images. The algorithms were combined with three invariant wavelet pyramids, a centered cubic spline pyramid as well as both low-pass and band-pass Simoncelli Steerable pyramids. This paper compared the different variations of the two algorithms on both synthetic and real satellite imagery. We found that for single-sensor data, the intensity-based algorithm combined with a band-pass wavelet pyramid produces the best results, while for multi-sensor images, the best choice is the mutual-information-based method combined with a steerable low-pass pyramid.
机译:精确的几何配准是在处理遥感图像的各种任务之前的重要步骤。假设经过放射线和系统的校正后,图像被配准到几个像素以内,我们的目标是为多传感器数据开发快速可靠的自动配准方法,以产生亚像素精度。本文比较了Thevenaz等人开发的两种基于梯度的亚像素图像配准算法。其中一个优化强度差,而另一个最大化两个图像之间的互信息。该算法与三个不变的小波金字塔,一个中心三次样条金字塔以及低通和带通西蒙切利可控金字塔相结合。本文比较了两种算法在合成和真实卫星图像上的不同变化。我们发现,对于单传感器数据,基于强度的算法与带通小波金字塔相结合可产生最佳结果,而对于多传感器图像,最佳选择是基于互信息的方法与可控的低点相结合。通金字塔。

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