Abstract
Aiming at the problems of insufficient image contrast in three-dimensional reconstruction of UAV in low illumination environment and the unstable iteration times of the RANSAC algorithm in the feature matching process, real-time matching method of UAV aerial image is proposed. First, a new image enhancement algorithm is applied to the image to enhance its quality and visibility. Second, the enhanced fast algorithm in ORB extracts the feature points from the preprocessed image, and cross-matching performs rough matching. Finally, the PROSAC algorithm solves the homography matrix by selecting the highest quality interior points from the extracted feature points. To improve the matching accuracy, some exterior points that do not conform to the geometric characteristics of the image are removed based on the homography matrix and the set mismatch threshold. The results show that the improved ORB algorithm is applied to the low illumination environment of UAV aerial photography, the image matching accuracy in 3D reconstruction is improved, and the correct matching rate tends to 97.24~99.39%. The relevant research findings and conclusions provide a fast and effective method for UAV image matching in different low illumination environments.
Highlights
Using the images taken by UAV to restore the threedimensional information of the scene has always been a hot issue in the image field
The results show that the realtime fast image matching method in UAV aerial photography low illumination environment has high execution efficiency and good robustness
In order to address the issue of poor image quality in a lowlight environment of UAV, which leads to a poor image matching effect, this paper first proposes an improved Retinex image enhancement algorithm to enhance and preprocess this type of image
Summary
Using the images taken by UAV to restore the threedimensional information of the scene has always been a hot issue in the image field. Zhu et al [24] proposed an improved method combining Harris corner detection, accelerated robust feature (SURF) feature detection (Harris + SURF) algorithm, and image enhancement algorithm to address the instability of image matching caused by the low quality and efficiency of images collected by illumination and complex terrain. (1) A novel adaptive weighted multiscale Retinex image enhancement algorithm is proposed to improve the quality of images taken in low illumination captured by UAV. The results show that the realtime fast image matching method in UAV aerial photography low illumination environment has high execution efficiency and good robustness. Combining the image enhancement algorithm and ORB algorithm applied to UAV frontend processing can improve the quality and matching accuracy of low illumination images
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