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Real-time stitching of UAV (Unmanned Aerial Vehicle) sensor images can effectively enhance the sensory ability of the ground station upon the task environment thereof. When UVA sensor images stitched at different time, if the moving object is located at the overlapping part of the adjacent images, the dislocation brought by the object movement may cause "ghosting" phenomenon of the moving object in the final stitched image and accordingly influences the final stitching effect. Therefore, a ghosting elimination method based on target location information was designed in this paper, specifically as follows: firstly, obtaining the location information of the moving object by the target detection method; then, adopting the improved Criminisi algorithm to automatically repair the image from which the dynamic object is eliminated; finally, stitching the images and intensively displaying the extracted target at the corresponding position in a form of three dimensional entity. In this paper, the proposed method was adopted for the real-time stitching test of UVA aerial images, and the experiment result has proved the effectiveness of the proposed method.

Images obtained by unmanned aerial vehicle (UAV) are superior in many aspects such as convenience, low cost and so on. However, the obtained attitude of the UAV is usually not accurate, which further leads to the decrease of efficiency and accuracy of the algorithm based on the position and attitude information. Despite the algorithm based on image feature can get a more accurate result, it usually requires a longer time to process the images and even can bring a large cumulative error when continuous image is stitched. This paper presents a new method for stitching panoramas from UAV's images which is mainly composed of three steps. Firstly, once the images have been obtained, global motion model for UAV aerial photography can be used to establish a predicted region. Second, features in the onboard images are matched by using SIFT algorithm in this region. Finally, the accuracy of image mosaic is predicted through the Grey Relational Analysis. Then, according to the result, the algorithm process can adjust itself automatically to meet the needs of different types of sensor image mosaic. We demonstrate through flight experiments that compared with conventional approach our method can stitch images accurately and rapidly and produce high-quality panoramas.