Image stitching method based on unmanned aerial vehicle POS information and image SURF feature combination

Abstract

The invention discloses an image stitching method based on unmanned aerial vehicle POS information and image SURF feature combination and relates to the digital image processing field, the GIS field,the survey field and other relevant fields. According to the method, first, geometric correction is performed on images; second, geographic coordinates of four corners of each image are calculated; based on the geographic coordinates of the first image, a position relation of homonymy matching target positions is obtained by extracting SURF features of adjacent image overlapping regions, and therefore the geographic coordinates of the following images are sequentially corrected; and last, an adaptive gradual-in-gradual-out fusion algorithm is adopted, a panoramic image with a good visual effect is obtained, and good stitching of the images is completed. Through the method, an image feature extraction algorithm and the geographic coordinates of the images are combined, and stitching efficiency and the visual effect are both improved greatly compared with traditional feature extraction and stitching algorithms; and the image obtained after stitching contains geographic information, so that the image has certain practical value.

Description

technical field [0001] The invention relates to a splicing method based on the combination of unmanned aerial vehicle POS (Position and Orientation System) information and image SURF features, and belongs to the fields of digital image processing, GIS, surveying and mapping, and other related fields. Background technique [0002] UAV (Unmanned Aerial Vehicle) has the characteristics of simple operation, quick response, flexible flight, and low cost. It has been widely used in disaster relief, military reconnaissance, maritime detection, environmental protection, and land dynamic monitoring. However, due to the limited viewing angle of UAV aerial photography sequence pictures, it is limited by the flying height of UAV and the parameters of the camera. In order to fully grasp and analyze the captured area and get more information about the target area, it is very urgent and necessary to quickly stitch images. [0003] At present, there are two main methods for stitching aeria...

AI Technical Summary

Problems solved by technology

This information records the attitude of the aircraft at each exposure moment when it is flying. However, since the attitude of the drone is not fixed when it is taking aerial photography, two sets of POS data of adjacent exposure points are needed for aerotriangulation calculation. In this way, the latitude and longitude of the image pixels are calculated, and the error is different with the establishment of the model.

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