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Improved Coregistration of Multidate and Multisensor Orchard Imagery

机译:改进了多人和多用户果园图像的核心简调

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Combining multi-date and multi-instrument remote sensing images can only yield good results when the images are coregistered very precisely. We present a practical method to improve coregistration of image pairs. It uses area based matching to build a local misregistration model, which is used to warp images to a reference. The challenge is to achieve good matching when images have vastly different appearance due to the instruments or acquisition conditions. The user can avoid errors by masking disturbing areas. Additionally, we propose a sharpness normalization method to facilitate matching. A transformation to an invariant representation is also introduced in order to reduce the effect of shadows. We demonstrate the effect of the methods to improve matching and improve coregistration.
机译:组合多日期和多仪器遥感图像只能在非常精确地注入图像时产生良好的结果。我们提出了改进图像对的核心简化的实用方法。它使用基于区域的匹配来构建一个本地误解模型,用于将图像扭曲到参考。当图像由于仪器或采集条件而言,当图像具有巨大不同的外观时,挑战是实现良好的匹配。用户可以通过掩盖令人不安的区域来避免错误。此外,我们提出了一种锐度归一化方法,以方便匹配。还引入了对不变表示的转换,以减少阴影的效果。我们展示了改善匹配和改善核心试卷的方法的效果。

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