关键词: 光学图像, SAR图像, 图像配准, 特征匹配, 模板匹配, 混合模型, SAR-SIFT算法, HOG特征, ROEWA算法

Abstract:

Due to the fact that optical remote sensing image and SAR image have obvious nonlinear intensity differences, and that SAR image has speckle noise, it is difficult to register them. Feature-based image registration and region-based image registration are the two most common methods of optical and SAR image registration. One advantage of feature-based image registration is that it can solve the problem of rotation, scale, and translation differences between images. Another advantage is the small amount of calculation. However, this method usually has the disadvantages of low registration accuracy and instability. Region-based image registration can achieve high-precision registration of heterogeneous images. However, it performs poorly for images with large rotations, scale differences, and it has heavy computation task. For these problem, this paper combines the advantages of feature-based and region-based image registration methods into a hybrid model and proposes an automatic registration algorithm for optical and SAR images. The optical remote sensing image is the reference image while the SAR image is the one to be registered. The SAR-SIFT based on the feature points is used to complete the coarse registration and then the ROEWA-HOG based on the region is used to complete the fine registration. Firstly, the SAR-SIFT algorithm, robust to nonlinear intensity differences and speckle noise, is used to perform feature point detection and feature matching to calculate the affine transformation model of the image to eliminate the obvious rotation, scale and translation difference between the optical image and the SAR image. This is the coarse image registration. Secondly, we use the block Harris corner detection method to obtain a certain number of evenly distributed feature points on the reference image. We determine the search area of the corresponding points on the image to be registered according to the feature points, calculate the ROEWA gradient of the image, and then use a fast calculation strategy to construct the HOG feature vector in the template area with the feature points as the center. Then, we use SSD as the similarity measure to search the corresponding points on the image to be registered. This is the high-precision image registration. Finally, we carry out the image registration and perform visual inspection and quantitative evaluation of the registration results. It is demonstrated that the algorithm in this paper can combine the advantages of feature-based and region-based image registration methods to better resist the noise effect of SAR images and the nonlinear intensity, rotation, scale, and translation differences between optic and SAR images. The final registration accuracy of our high-precision automatic registration method is 1 pixel. High-precision automatic registration for SAR images can meet subsequent comprehensive applications of optical and SAR images.

Key words: optical image, SAR image, image registration, feature matching, template matching, hybrid model, SAR-SIFT algorithm, HOG feature, ROEWA algorithm