Registration of multitemporal low-resolution synthetic aperture radar images based on a new similarity measure

Image registration is concerned with the precise overlap of two images. One challenging problem in this area is the registration of low-resolution synthetic aperture radar (SAR) images. In general, extracting feature points from such images is difficult due to the coarse observation and the severe speckle. The use of area similarity for image registration is another important branch to solve the problem. A similarity measure based on a conditional density function (cdf) is proposed. The cdf is specially tailored for SAR images, where the speckle is generally assumed as multiplicative gamma noise with unit mean. Additionally, a two-step procedure is devised for the registration of intro-model SAR images to improve the computational efficiency. First, the two images are roughly aligned considering only the translational difference. Then small blocks from the two images are accurately aligned and the center point of each block is treated as a control point, which is finally used to obtain the precise affine transformation between the two images. Five SAR image datasets are tested in the experiment part, and the results demonstrate the efficiency and accuracy of the proposed method.