Algorithm of remote sensing image matching based on corner-point

Feature extraction is an important method to obtain remote sensing image information. It has significant influence on recognition, analysis, matching, fusion, segmentation of remote sensing image. Image registration is usually classified into two categories: the intensity-based method and the feature-based method. The corner-point is one of the basic features of the images, which has many information and can easily be detected. In the area of remote sensing image application, two or more images are usually mosaiced as one image. According to remote sensing image matching, a method of image matching based on Harris corner-point combined with SURF algorithm is proposed in this paper. First of all, feature points are detected using Harris algorithm, that has the ability of noise immunity and stability. Then, calculating by SURF algorithm, the main directions of the feature points are determined and the feature descriptors are generated. Ratio method is used to get initial matching, and RANSAC algorithm is used to eliminate errors and achieve accurate matching. The result demonstrates that the Harris corner-point image registration described is stable and efficient. The method can be well applied in the remote sensing image processing and geometric positioning accuracy evaluation.