Research On Automatic Registration Algorithms For Infrared And Visible Remote Sensing Images

Image registration is the process of overlaying two or more images of the same scenetaken at different times, from different viewpoints, and/or by different sensors.Infrared-visible remote sensing image registration is an important combination form ofmulti-sensor image registration. Organic integration of the two types of images canenhance the complementarity of the scenes information, and reduce the uncertainty forscene understanding. Therefore, Infrared-visible remote sensing image registration hasreceived extensive attention in many application fields, such as military intelligenceacquisition, autonomous navigation, terminal guidance, target tracking, image fusion,change detection, environment monitoring and so on.There exist obvious visual differences between the infrared and visible images of thesame scene due to their different imaging mechanisms. Other influence factors, moreover,may also occur between the images, for example, geometric deformation, partial occlusionand noise perturbation, etc. These make the automatic registration of the infrared andvisible images challenging. This dissertation starts from the spectral characteristics ofinfrared ray and visible light, analyses their imaging characteristics, explores thedifferences and similarities between infrared and visible images, and summarizes twocommonly used image registration frameworks. Based on this knowledge, a variety ofinfrared-visible remote sensing image registration methods are proposed, and theeffectiveness of these methods are validated and analysed throuth comparative experiments.The main achievements and contributions of the dissertation are listed as follows:(1) A novel local invariant feature description opeartor, Windowed IntensityDifference Histogram (WIDH), is proposed. The opeartor can effectively utilize intensitydifference information of the local area around the feature to construct descriptive vectors.The experimental results show that the descriptor has good invariance to luminance change,structure blurring, geometrical distortion and noise disturbance, and possesses lowdimensionality but strong discrimination. Subsequently an automatic registration algorithmfor near-infrared and visible remote sensing images is proposed based on blob features andWIDH operator. The algorithm detects blob features using SURF detector, costructsWIDH description vectors for the blobs around their local neighborhoods, and identifiescorresponding relationships between the vectors based on the nearest neighbor searchalgorithm in terms of the Euclidean distance criterion. Finally the incorrectcorrespondences are removed by a Random Sample Consensus step and then thetransformation parameters are determined. The experimental result shows that theproposed algorithm can realize the registration of near-infrared and visible images moreeffectively than the SURF-based algorithm. (2) An automatic registration algorithm for infrared-visible remote sensing images isproposed based on junction point matching. At the feature detection step, a novel junctionpoint detection and characterization algorithm is prosposed based on azimuth consensus.The extracted junction points possess not only position information, but also local structureinformation, such as the number and slope angles of branch edges. The experimentalresults show that the algorithm has high accuracy in junction localization, and haspreferable robustness to noise disturbance and contrast change. At the junction pointmatching step, the conception of local structure compatibility is proposed to verify theconsistency degree of the number and slope angles of branch edges between two junctionpoints. As constraint terms, Local structure compatibilities are embedded in the posteriorprobability computation of GMM (Gaussian Mixture Models) components. This canreinforce the algorithm’s resistibility to the disturbance caused by noises and outliers, andcan improve the convergence speed of the algorithm.(3) An automatic registration algorithm for infrared-visible remote sensing images isproposed based on parameter step estimation. The parameters of affine transformationmodel is separated into some more easily estimated factors using matrix orthogonaldecomposition method, which are skew, scale ratio, rotation, scaling and translations in xand y directions. These six factors are categorized into to two groups. The first groupincludes skew, scale ratio and rotation, and the second group includes scaling andtranslations in x and y directions. Subsequently the values of the parameters in two groupsare estimated step by step using segment features extracted in the images. At the first step,an objective function with the skew, scale ratio and rotation factors as variablesconstructed based on the consensus model of the segments’ orientations. The optimalsolutions are obtained by an optimization method at the given parameter space. After thefirst step, the affine transformation is simplified to the similarity transformation. At thesecond step, another objective function, with respect to scaling and translations in x and ydirections, is constructed by the alignment degree between segments. The optimalparameter values of the second group are computed by the same optimization method asthe fisrt step. A hybrid strategy is adopted in the optimization process, which is combinedby the simulated annealing algorithm and the improved Powell algorithm. Theoptimization strategy guarantees the globality and accuracy of the results effectively. Theproposed algorithm simplifies the6D parameter space of the affine model to two3Dspaces through the parameter step estimation method, which can reduce the size of thesearching space significantly, and can effectively lower the properbability of theoptimization process trapped into local optimum.(4) An automatic registration algorithm for infrared-visible remote sensing images isproposed based on optimal mapping of edge structures. The algorithm selects thecontinuous edge structures in the image as registration elements, which can suppress the influence of noises and inconsistent information. The registration problem is transformedinto an optimization problem in the parameter space by constructing an alignment measurefunction with respect to the edge structures. A similar hybrid framework is employed in theoptimization process. The approximate global optimum is obtained by the parallel geneticalgorithm, which is followed by the improved Powell algorithm to refine the solutionlocally. To improve the efficiency of the registration algorithm, a flexible framework isproposed, which combines hierarchy approximation models in the two-scale space. Affineand projective transformation models are used in the coarse and fine scales respectively.The proposed algorithm has good adaptability and robustness due to the use of morecommon structure features in images and the integration of more flexible projective model,and reduces the probability of misregistration caused by lack of the particular registrationelements.