Determination of platform attitude through SURF based aerial image matching

In this paper, an improved image matching methods based on Speeded Up Robust Features (SURF) algorithm was proposed to calculate the attitudes of the aircraft platform. Firstly, SURF algorithm was used to extract series of the interest points of two selected images. Then the improved nearest neighbor matching method based on KD-tree was used to get the pairs of the matching interest points. And the RANdom SAmple Consensus (RANSAC) algorithm was used to remove the error pairs of matching interest points. Finally, the transformation matrix between the two selected images was established to obtain the attitude angles. In order to validate the effectiveness, two images were utilized to implement the proposed methods. The experimental results show that the algorithm has the advantage in both accuracy and time-consuming, which is helpful to improve the performance of image navigation.