Aim to the difficulty of automatic and robust registration of optical imagery with point cloud data, this paper propose a new method based on SIFT and Mutual Information (MI). The SIFT features are firstly extracted and matched, whose result is used to derive the coarse geometric relationship between the optical imagery and the point cloud data. Secondly, the MI-based similarity measure is used to derive the conjugate points. And then the RANSAC algorithm is adopted to eliminate the erroneous matching points. Repeating the procedure of MI matching and mismatching points deletion until the finest pyramid image level. Using the matching results, the transform model is determined. The experiments have been made and they demonstrate the potential of the MI-based measure for the registration of optical imagery with the point cloud data, and this highlight the feasibility and robustness of the method proposed in this paper to automated registration of multi-modal, multi-temporal remote sensing data for a wide range of applications.
This paper proposes a robust matching method for the multi-sensor imagery. Firstly, the SIFT feature matching and relaxation matching method are integrated in the highest pyramid to derive the approximate relationship between the reference and slave image. Then, the normalized Mutual Information and multi-grid multi-level RANSAC algorithm are adopted to find the correct conjugate points. Iteratively perform above steps until the original image level, the facet- based transformation model is used to carry out the image registration. Experiments have been made, and the results show that the method in this paper can deliver large number of evenly distributed conjugate points and realize the accurate registration of optical and SAR multi-sensor imagery.
In this paper, we propose an automatic and accurate image registration method for the high resolution. Due to the strong
distortion caused by the terrain relief in this kind of images, it cannot be resolved by one set of transformation
coefficients for the whole image. So the method mainly consists of two parts: one part is the dense feature point
matching, and the other is the faced based differential registration. The matching algorithm integrates the feature point
matching, relaxation optimization technique, the Least Square Matching, the coarse-to-fine strategy, and it can provide
hundreds of thousands of reliable and accurate control points. With the TIN, these points divide the image into a lot of
small triangles. For each triangle, we can assume the local distortion is simple and can be depicted by the affine
transformation function. Finally, faced based differential registration is performed to resample the slave image.
Experiments have been carried out and satisfactory results have been obtained.
In this paper, we present a matching method for DSM generation from multiple images based on feature points, which
introduce the coarse-to-fine strategy, geometrically constrained matching and relaxation technology, the matching is
guided by the information in the object and make full use of the information in both image and object space. A match
appearing in any pair has the chance to survive, and very dense disparity maps are obtained. Experiments have been
performed and the height accuracy of the derived DSM is about 3 pixels.