A method for splicing UAV images

Abstract

The invention discloses a method for splicing images of unmanned aerial vehicles, comprising: obtaining SIFT feature matching pairs of images to be matched captured by unmanned aerial vehicles, and obtaining initial transformation parameters of other images to be matched; assuming that the transformation parameters of the images are set to x i , x i represents a column vector consisting of 8 independent parameters of the homography matrix, T i for x i Expressed transformation, let define the objective function: E(X)= c E( o x r )+ω(EX) r ; Among them, ω is a constant weight; E cor (x) is based on the energy item corresponding to the feature point, E rig (x) is the constraint item of the transformation parameter x; the features of the SIFT feature matching pair are substituted into the objective function, and the LM algorithm is used for optimization; the transformed image is fused. The UAV image mosaic method of the present invention can keep each transformation parameter as a rigid transformation as much as possible, so that the result of mosaic does not undergo global deformation, makes each image try to maintain the original shape, and makes the global deformation of the output mosaic image effective control, and without the need for camera parameters and pose information, a good stitching effect can be obtained.

Description

technical field [0001] The invention relates to the technical field of UAV image processing, in particular to a method for mosaicing UAV images. Background technique [0002] UAV reconnaissance technology is a kind of remote sensing technology, which has its own advantages compared with satellite reconnaissance, such as low cost, flexible reconnaissance area control, no access time and cycle restrictions, and high resolution of ground targets; compared with manned reconnaissance aircraft, It has the advantages of continuous work day and night, regardless of pilot fatigue and casualties. In recent years, due to the advantages of high resolution, high flexibility, high efficiency and low cost of low-altitude remote sensing data, UAVs have been widely used in natural disaster area assessment, battlefield reconnaissance, environmental monitoring and other fields. Although UAV reconnaissance technology has played an important role in areas such as assessment of natural disaster ...

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Problems solved by technology

Although this algorithm brings a good registration effect to image registration, it does not improve the image stitching speed much. For large-scale UAV reconnaissance images, it is necessary to change the parameters of the SIFT algorithm. It is more difficult to achieve the effect of real-time image processing, and UAV image stitching usually requires sensors such as GPS / IMU to provide the attitude information of the camera, or use image-based 3D reconstruction methods (such as structure-from-motion) to restore the camera's attitude. Attitude, the calculation efficiency is relatively low, and the image effect after splicing is not good

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