Research On UAV Aerial Image Mosaic Technology

Unmanned aerial vehicle(UAV)aerial photography is the basis of forest fire prevention,agriculture and forestry plant protection,emergency rescue and other tasks.Due to the low flight altitude and the limited imaging detection range of the UAV,the application needs to expand the imaging detection range through image mosaicking.However,the commonly used registration method based on image features is difficult to work effectively in characterless scene or time-sensitive scene.The registration method based on grayscale information does not have the ability to handle the transformation such as multi-perspective rotational affine of UAV,and the registration method based on position and pose information has a large registration error under the limitation of sensor accuracy.In addition,the global(especially in the overlapping joints)color natural transition is need after registration.However,the haze weather or image brightness varies greatly due to the change of perspective,it is difficult to realize the global color correction,such as histogram specification and wallis filtering,or the fusion methods such as direct average fusion,gradual in and out fusion,and optimal suture fusion.According to the above problems,a new method of image registration based on multiple gray-scale similarity measure voting and a mosaic image quality optimization method based on image degradation model are proposed.The specific study work is summarized as follows:1)Gray-scale similarity measure voting is adopted to realize fast and reliable registration in characterless scene or time-sensitive scene.First of all,the initial mapping of the partial pixels between the reference image and the mosaic image are quickly established by the position and pose information of the UAV.Secondly,the position of the mapping points are corrected by voting on the three graysacle similarity measures:sum of differences,mutual information and histogram correlation coefficient.Finally,the modified mapping points are used to solve the homography to achieve accurate image registration.The experimental results show that the registration method based on the gray-scale similarity measure voting can work effectively in the scene with few image features,and the time consumption is only about 15%of the image registration method based on SIFT feature extraction,and the registration accuracy is better than the registration method based on gray information and the registration method based on position and pose information.2)Correcting the transmittance difference based on the image degradation model to ensure the natural brightness transition at the joint of the mosaic image under the brightness difference scene or haze weather.The dark channel prior algorithm is used to estimate atmospheric light.Combined with the image degradation model,to derive the relationship between image brightness and atmospheric light and atmospheric transmission.Then,the brightness of the image with color differences is balanced or the haze image is enhanced to generate a panoramic mosaic image.The experimental results show that compared to methods,such as histogram normalization,wallis filtering,direct average fusion,gradual fusion,and optimal suture fusion,the images corrected for transmittance have smaller mean difference and standard deviation in dense fog and color difference scene.3)The new registration method and mosaic image quality optimization method are applied to drone swarm images with different flight atitudes and perspectives.The experimental results show that in order to exploit the advantage of rapid detection of drone swarm,it is necessary to reduce the overlapping fields of different positions.The new registration method is more reliable in the scene with rare image features,and overcome the brightness difference of drone swarm.In summary,the registration method and the mosaic image quality optimization method have the comprehensive advantages of accuracy,robustness and speed,which can work effectively in characterless scene,time sensitive scene,haze scene,swarm detection scene,for UAV or drone swarm in forest fire protection,agriculture and forestry,emergency protection and other applications to provide new aerial image processing technology support.