An algorithm of parameters adaptive scale-invariant feature for high precision matching of multi-source remote sensing image

This paper proposes a new method to deal with visual information expression of image formation and systematization based on visual information representation theory, and analyzes the characteristics of multi-source remote sensing image from the perspective of remote sensing imaging mechanism, and expatiates some pivotal rules regarding visual information during image capturing, description and reconstruction. In the process of formulating SIFT description, this paper makes a detailed research on how to calculate the lower matching pints and marginal points, adjust the threshold of the feature matching parameters and increase the matching points numbers automatically, based on the number of the exiting matching points and their distribution conditions. In this experiment, the number of feature points increases with the decrease of the threshold of low contrast points, and edge response points, which shows the similar changes in the Law of Inverse; While in the process of automatic matching, the number of feature points increases with the increase of radio value of the farthest distance of the feature points to the nearest distance, showing almost directly proportional to the law. In general, as the number of matching points increase, the accuracy and the stability of the matching would decrease. This paper proposes a threshold weight of the adaptive algorithm to improve the accuracy and robustness of the matching points and solves the problems described above. Therefore, the multi-source remote sensing images are generally divided into the images with same resolution and those with different resolutions. When the reference image and the uncorrected image have the same resolution, the connection lines of the matching points will have the same distance and slope. By contract, when the resolution of the reference image and that of the uncorrected images are different, their connection lines of matching points will intersect. This paper, studying this geometric constraint conditions, suggests a fast mismatching points' rejected method based on rough fuzzy C-Means cluster theory. This paper then discusses the precise matching of residual matching points using Least Square Method. Numerous experiments are conducted for both aerial and satellite imageries under various conditions such as geometric distortion, illumination variation and different resolutions. Results of this study show that the proposed matching approach performs well, and the matching accuracy is