Dept. of Electr. and Comput. Eng., California Univ., Santa Barbara, CA.
IEEE Trans Image Process. 1995;4(3):320-34. doi: 10.1109/83.366480.
Image registration is concerned with the establishment of correspondence between images of the same scene. One challenging problem in this area is the registration of multispectral/multisensor images. In general, such images have different gray level characteristics, and simple techniques such as those based on area correlations cannot be applied directly. On the other hand, contours representing region boundaries are preserved in most cases. The authors present two contour-based methods which use region boundaries and other strong edges as matching primitives. The first contour matching algorithm is based on the chain-code correlation and other shape similarity criteria such as invariant moments. Closed contours and the salient segments along the open contours are matched separately. This method works well for image pairs in which the contour information is well preserved, such as the optical images from Landsat and Spot satellites. For the registration of the optical images with synthetic aperture radar (SAR) images, the authors propose an elastic contour matching scheme based on the active contour model. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying the active contour model. Both contour matching methods are automatic and computationally quite efficient. Experimental results with various kinds of image data have verified the robustness of the algorithms, which have outperformed manual registration in terms of root mean square error at the control points.
图像配准关注的是同一场景的图像之间的对应关系的建立。该领域的一个具有挑战性的问题是多光谱/多传感器图像的配准。一般来说，这种图像具有不同的灰度特征，简单的技术，如基于区域相关的技术，不能直接应用。另一方面，在大多数情况下，代表区域边界的轮廓得以保留。作者提出了两种基于轮廓的方法，它们使用区域边界和其他强边缘作为匹配基元。第一种轮廓匹配算法基于链码相关和其他形状相似性标准，如不变矩。闭合轮廓和沿开轮廓的显著段分别进行匹配。这种方法对于轮廓信息保存良好的图像对效果很好，例如 Landsat 和 Spot 卫星的光学图像。对于光学图像与合成孔径雷达 (SAR) 图像的配准，作者提出了一种基于主动轮廓模型的弹性轮廓匹配方案。使用光学图像中的轮廓作为初始条件，通过应用主动轮廓模型可以获得 SAR 图像中的准确轮廓位置。两种轮廓匹配方法都是自动的，计算效率相当高。使用各种类型的图像数据进行的实验结果验证了算法的鲁棒性，在控制点处的均方根误差方面，算法的性能优于手动配准。
