Image registration concerns the problem of how to combine data and information from multiple sensors in order to achieve improved accuracy and better inferences about the environment than could be attained through the use of a single sensor. Registration of imagery from multiple sources is essential for a variety of applications in remote sensing, medical diagnosis, computer vision, and pattern recognition. In general, an image registration methodology must deal with four issues. First, a decision has to be made regarding the choice of primitives for the registration procedure. The second issue concerns establishing the registration transformation function that mathematically relates images to be registered. Then, a similarity measure should be devised to ensure the correspondence of conjugate primitives. Finally, a matching strategy has to be designed and implemented as a controlling framework that utilizes the primitives, the similarity measure, and the transformation function to solve the registration problem. The Modified Iterated Hough Transform (MINT) is used as the matching strategy for automatically deriving an estimate of the parameters involved in the transformation function as well as the correspondence between conjugate primitives. The MIHT procedure follows an optimal sequence for parameter estimation. This sequence takes into account the contribution of linear features with different orientations at various locations within the imagery towards the estimation of the transformation parameters in question.; Accurate co-registration of multi-sensor datasets captured at different times is a prerequisite step for a reliable change detection procedure. Once the registration problem has been solved, the suggested methodology proceeds by detecting changes between the registered images. Derived edges from the registered images are used as the basis for change detection. Edges are utilized because they are invariant regardless of possible radiometric differences between the images in question. Experimental results using real data proved the feasibility and robustness of the suggested approach.
展开▼
