Study On Registration For SAR And Optical Satellite Image

Image registration is a research focus in many fields such as computer vision, pattern recognition, medical image analysis and remote sensing image processing, etc. After analyzing the difficulty of multi-sensor images registration, we focus on the study of automatic registration for SAR and optical satellite images. The related work and mainly contribution as follows:1. SAR images segmentation. SAR images segmentation as the most important step of closed-region abstracting is been emphasized in this thesis. Based on an analytical review of several classic segmentation methods, two novel algorithms are proposed for SAR segmentation. The first one is named combine MAP (Maximum a Posterior) criterion and SRAD (Speckles Reducing Anisotropic Diffusion) model which belong to the kinds of method segmenting SAR images using structures and intensity information. These kinds of method reducing speckle during the process of segmentation and having a high accuracy are fit for greater number of looks SAR images segmentation. The second one is called segmentation based on adaptive regularizing non-linear diffusion equations which belong to the other kinds of method that segment SAR images despeckled using method segmenting optical images. These methods are applied for big size and lower number of looks SAR images. We compromise these two methods according to the size and resolution of images.2. Optical image filtering and segmentation. Optical images always corrupted by additive Gaussian white noise, so we have to improve quantity of images by filtering and enhancing. A class of fourth-order partial differential equations is used to optimize the trade-off between noise removal and edge preservation. Since the Laplacian of an image at a pixel is zero if the image is planar in its neighborhood, these PDEs attempt to remove noise and preserve edges by approximating an observed image with a piecewise planar image. Piecewise planar images look more nature than step images. Then C-means is used for segmenting filtered optical image.3. Registration using Hausdorff distance and Genetic Algorithm. A registration method using Hausdorff distance and Genetic Algorithm is proposed. After exstrcting closed regions from SAR and optical satellite image, Hausdorff distance and genetic algorithm are applied to estimate the parameters of transformation between two images. We separate the process of searching five parameters in two steps in order to ensure that there are no more than three parameters in each search step. This way can enhance the searching speed. At the last, registration refinement based on binary correlation is used for solving mild distortion problem.