卫星图像配准及匹配曲线特征评估法

Satellite Image Registration and Matching Curves Feature Assessment Method

由于卫星图像来自于不同的传感器、由不同的视角和光谱、在不同的时间获得,图像间存在较大差异。为了有效配准图像,提出一种"先粗后精"的配准算法,首先采用Fourier-Mellin变换算法实施快速的粗配准,然后采用以修正的结构相似度为测度的优化算法实施精确配准。对于真实的卫星图像配准,由于没有准确的衡量标准,很难给出定量的评估结果。本文提出一种新的配准评估方法?匹配曲线特征评估法,以匹配曲线的峰度、峰偏、峰值以及峰值间均方根误差(RMSE)为定量评估指标,以峰值间RMSE最小为准则自动调整配准参数。结果表明,"先粗后精"的配准算法能够实现相当精确的配准;匹配曲线特征评估法不仅能够从曲线的光滑度、尖锐度等特性直观描述配准性能,并能由曲线的特征指标定量评估配准效果,而且还能自动调整配准参数,使配准更加精确。

The registration of satellite imagery is challenging task because of considerable differences between the image pairs captured by different sensors, view angles, spectrum or at different times. To align the images effectively, a coarse-to-fine registration algorithm is proposed. First, Fourier-Mellin transform algorithm is used to implement a fast coarse registration. Then, the optimization algorithm based on MSSIM measure is used to implement a fine registration. Because there is no accurate measure standard, it is very difficult to give a quantitative assessment of the registration results for real satellite images. A novel registration evaluation method is proposed, called Catching Curve Feature Evaluation （MCfe） method. In MCfe, kurtosis, peak deviation, peak maximum and Root Mean Square Error （RMSE） among peak maxima are extracted as the quantitative evaluation indexes, and the minimizing on RMSE is used as a registration criterion. The results demonstrate that the coarse-to-fine registration algorithm can achieve an extremely accurate registration for satellite imagery. The MCfe method can intuitively describe the registration performance from the curve features such as smoothness and sharpness, and also to be used to quantitatively assess the registration results, but also to automatically adjust the registration parameters to obtain a more accurate registration.