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Multispectral imagery registration based on minor and noise component criterion

机译:基于次要分量和噪声分量准则的多光谱图像配准

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For multispectral imagery, spatial registration between bands is a very important part of the overall quality of the multispectral imagery product. Due to the significant differences in scene reflectance at different wavelengths , mostly multispectral imagery registration methods are unreliable. In this paper, a robust multispectral imagery registration method is presented. As we know, the spectral information (endmembers) of some pixels will been confused when multispectral imagery is mis-registered. The change of confused endmembers can be estimated through observing minor eigenvalues. Based on this property, a minor and noise component criterion is defined. The best alignment is reached when their minor and noise component is at its minimum. Experiments were conducted using multispectral imagery from the ETM Satellite. And corresponding registration performance curve is given. Multispectral imagery is pre-rcgistered with the method of normalized mutual information. Computer simulations show that the normalized mutual information method may have a deviation about 10 pixels between a pair of images. The method we presented can have a deviation less than 1/4 pixel. Registering curve show that this method is efficient and robust, it can be used for precise multispectral imagery registration.
机译:对于多光谱图像,波段之间的空间配准是多光谱图像产品整体质量中非常重要的一部分。由于在不同波长下场景反射率存在显着差异,因此大多数多光谱图像配准方法都不可靠。本文提出了一种鲁棒的多光谱图像配准方法。众所周知,当多光谱影像配准错误时,某些像素的光谱信息(端成员)将会被混淆。可以通过观察次要特征值来估计混乱的末端成员的变化。基于此属性,定义了次要和噪声成分标准。当它们的次要分量和噪声分量最小时,可以达到最佳对准。实验是使用来自ETM卫星的多光谱图像进行的。并给出了相应的配准性能曲线。多光谱图像通过归一化互信息方法进行了预先配置。计算机仿真表明,归一化的互信息方法在一对图像之间可能有大约10个像素的偏差。我们提出的方法的偏差可能小于1/4像素。配准曲线表明该方法高效,鲁棒,可用于精确的多光谱图像配准。

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