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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features
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Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features

机译：具有全局Shearlet功能的多模式遥感数据的自动图像配准

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摘要

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard but are sometimes not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered and then performs least-squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, although approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared with wavelet features alone.
机译：自动图像配准是在最少的人工帮助下对齐大致相同场景的两个或更多图像的过程。基于小波的自动配准方法是标准的，但有时对初始条件的选择不可靠。即，如果要配准的图像相对于该算法的初始猜测相距太远，则配准算法不会收敛或具有较差的精度，因此不够鲁棒。出现这些问题的原因是，小波技术主要识别各向同性的纹理特征，而识别线性和曲线边缘特征的效率较低。我们将现有的基于小波的自动配准算法集成了最新开发的小波的数学构造，该结构在识别稀疏各向异性边缘方面更有效。我们的小波特征算法比小波特征算法产生更多不同的特征。边缘与纹理的分离甚至比小波更强。我们的算法计算待配准图像的剪切波和小波特征，然后对这些特征执行最小二乘最小化以计算配准变换。我们的算法本质上是两阶段多分辨率的。首先，使用一系列的小波特征来提供鲁棒的（尽管近似）配准。然后通过注册级联的小波特征对其进行完善。与单独的小波特征相比，针对各种图像类别的实验显示出对初始条件的增强的鲁棒性。

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