Remote Sensing Image Registration And Fusion Technology Research

Remote sensing image registration and fusion is an important image processing technology in remote sense field. Remote sense fusion can integrate data from different sensor, different resolution, different spectrum and different time, eliminate redundancy and integrate complementary to get more accurate and more abundant image information than a single information resource, which improves the utilization efficiency and solves the mass remote sense data problem effectively. However, registration of remote sensing image is the basic precondition of fusion, image fusion can not be implemented effectively without accurate registration. Remote sensing image registration plays an important role to fusion.In terms of registration, this paper aims at the difficulty of registration between images with large geometry deformation and spectrum difference, and utilizes scale-invariant SIFT features and Harris-Laplace features in scale space to develop two methods of image automatic registration. For SIFT registration, this paper analyses the scale-invariance of SIFT features, and adopts circle neighbor based main orientation to improve the rotation invariance of features which makes registration be achieved at random rotation. Feature space imitates the model of biological complex neurons in primary visual cortex, and it is constructed through orientation histogram in feature's local area. Feature matching combines distance match with position clustering, k-d tree is used to search the nearest features, which improves matching efficiency greatly. Simplified general HOUGH transform based on features'main orientation is proposed to implement feature cluster, which accelerates clustering and reduces the memory required. For Harris-Laplace registration, this paper analyses scale-invariance of characteristic scales in detail,proposes scale space projection to unify multi-scales to single scale and constructs normalized feature space as SIFT descriptors which makes feature space scale, rotation and illumination invariant. Feature matching combines distance match with RANSAC. Parameters in the registration procedure are optimized through experiments. The registration experiments results prove that the two methods can implement remote sensing registration automatically and accurately between remote sensing images with different rotation, different resolution and different spectrum.In respect of fusion, this paper adopts Laplacian pyramid decomposition and reconstruction to realize remote sensing image fusion, and summarizes tens of multi-resolution fusion rules. High-Frequency-Emphasis is utilized to fusion which gets a good fusion result. A lot of experiments are taken to analyse how the fusion levels and the neighbor size affect the fusion results through subjective and objective evaluation and computation efficiency. The fusion conclusions in this paper can apply to other multi-spectrum image fusion easily and have the use for reference in a certain sense.