为了提高高分辨率遥感图像配准的精确度,将非采样Contourlet变换应用于高分辨率遥感图像配准算法中。首先对高分辨率遥感图像进行非采样Contourlet变换,利用非采样Contourlet变换的平移不变性在变换域提取图像的边缘并选择合适的阈值准确地得到图像的边缘特征点。然后利用归一化互相关匹配法和概率支撑法对特征点进行匹配。最后通过三角形局部变换映射函数实现图像配准。实验结果表明,该方法更能准确地提取高分辨率遥感图像的特征点,大大提高了正确匹配的概率,与基于小波方法的图像配准效果相比有更高的准确性和稳健性。

For the purpose of improving high-resolution remote sensing images registration precision,nonsubsampled contourlet transform is applied to high-resolution remote sensing images registration. Firstly,for nonsubsampled contourlet transform shift invariance,nonsubsampled contourlet transform is used to extract the images edge in contourlet transform domain and the feature points are extracted from images edge by selecting an appropriate threshold. Then,normalized cross-correlation matching and probability support matching method are used to match the images feature points. Finally,triangle-based local transformation function is employed to register the images. The experimental results show that this method can more accurately extract the corresponding feature points of high-resolution remote sensing images and increase correct matching probability and have more precise and more robust registration effect than the method based on wavelet.