Research On Key Technologies Of Multi-sensor Images Matching

Matching multi-sensor images is an important work for vision based navigation and guidance, remote sensing based environmental monitoring and medical image analysis. In multi-sensor imagery, the relationship between the intensity values of the corresponding pixels is complex and unknown. Contrast reversal may occur in some regions, and the contrasts of the images may differ from one another. The multiple-intensity values in one image may map to a single intensity value in another. Further, features present in one image may not appear in another, and vice versa. The matching of multi-sensor images thus constitutes a challenging problem.In this dissertation, the vision navigation and guidance based on scene matching are taken as the research background, and the robust and real-time multi-sensor images matching system is taken as the research content. The emphatically work of this dissertation is on the key technologies of multi-sensor images matching.The kernel content of this dissertation is composed of the following five parts.In the first part, the adaptive NL-means filter is studied. The traditional image denoising methods are reviewed. The NL-means filter is introduced and a novel adaptive NL-means filter is proposed. The adaptive NL-means filter can denoise image with unknown noise distribution. And, in most cases, the adaptive NL-means filter can get better denoising result than original NL-means filter. In addition, the adaptive NL-means filter can also be applied on SAR images with speckle noise effectively.In the second part, the gravity model based edge matching method is studied. Traditional edge matching methods are introduced. As these methods are susceptible image noise and deformation, the author proposes a more robust method based on gravity model. The gravity vectors between point and point, point and point set, point set and point set are defined. The magnitude of gravity vector describes the similarity of the edge sets, while the direction of gravity vector can alleviate the search complexity. The author powers the gravity model by the consistency of normal direction of edge to further improve the reliability of edge matching.In the third part, the spatial structure feature is studied and a novel spatial sub-region congruency feature is proposed. The spatial sub-region congruency feature is constructed by the similarities among adjacent sub-regions. It can describe the structure of image while not have to detect edges or local maximums. Compared to traditional gradient based features, the spatial sub-region congruency feature is more robust to image noise, and hence fit to match multi-sensor images with inferior imagery quality.In the fourth part, rotate-invariant features are studied. A novel rotate-invariant feature, the Multi-scale Histogram of Angle of Radius and Gradient (MHARG) is proposed. The MHARG is not only invariant to image rotation, but is very robust to the nonlinear transform of the gray level and image noise. So, it is naturally suit for multi-sensor images matching. When matching by MHARG feature, the search process in the rotation space can be saved, thereby reducing the computational time substantially.In the fifth part, the refined matching on the basis of control region is studied. The main contributions are as follows. (1) A novel method to evaluate the ability of control region to be used for matching is proposed based on oriented Moran information. (2) As the reliability and precision of multi-sensor images matching are always lower than mono-sensor images matching, the author proposes a novel method for robustly solve the transformation parameters. (3) The author geometrically corrects the reference image and carries the refined matching on the corrected image iteratively. The iterative refined processes can further improve the matching precision.