影像匹配是诸多遥感影像处理和影像分析的一个关键环节。 传统基于角点的灰度相关匹配算法由于不具备旋转不变性而需要人工干预进行粗匹配,无法实现自动化。SIFT(scale invariant feature transform)算法能很好地解决图像旋转、缩放等问题,但是对于几何结构特征更加清晰、纹理信息更加丰富的高分辨率遥感影像而言,该算法消耗内存多、运算速度慢的问题非常突出。将两者结合,提出基于Harris角点和SIFT描述符的影像匹配算法。实验结果表明,相比SIFT算法,该算法大量缩减了运算时间,同时保留了SIFT描述符的旋转不变性和对光照变化的适应性,克服了灰度相关算法无法实现全自动的缺点,在高分辨率遥感影像匹配上效果较好。

Image matching is a fundamental step in remote sensing image processing and analysis. The traditional gray correlation coefficient matching algorithm does not have the rotation invariant feature. SIFT (scale invariant feature transform)algorithm can provide robust matching which is invariant to image scale and rotation. However, for high-resolution remote sensing images with clearer geometric structure and richer texture information, the problem of consuming large memory and slow computing is very prominent. In this paper, the image matching algorithm based on Harris corner and SIFT descriptor is proposed. The experimental results show that, compared to the SIFT algorithm, this algorithm greatly reduces the running time. It preserves the invariance of rotation and change in illumination by using SIFT descriptor, overcomes the shortcomings of the gray correlation coefficient matching algorithm, and has good performance on high-resolution remote sensing image matching.