Toward Real Time Uavs’ Image Mosaicking

Abstract

ABSTRACT:Anyone knows that sudden catastrophes can instantly do great damage. Fast and accurate acquisition of catastrophe information is an essential task for minimize life and property damage. Compared with other ways of catastrophe data acquisition, UAV based platforms can optimize time, cost and accuracy of the data acquisition, as a result UAVs' data has become the first choice in such condition. In this paper, a novel and fast strategy is proposed for registering and mosaicking of UAVs' image data. Firstly, imprecise image positions are used to find adjoining frames. Then matching process is done by a novel matching method. With keeping Sift in mind, this fast matching method is introduced, which uses images exposure time geometry, SIFT point detector and rBRIEF descriptor vector in order to match points efficiency, and by efficiency we mean not only time efficiency but also elimination of mismatch points. This method uses each image sequence imprecise attitude in order to use Epipolar geometry to both restricting search space of matching and eliminating mismatch points. In consideration of reaching to images imprecise attitude and positions we calibrated the UAV's sensors. After matching process, RANSAC is used to eliminate mismatched tie points. In order to obtain final mosaic, image histograms are equalized and a weighted average method is used to image composition in overlapping areas. The total RMSE over all matching points is 1.72 m.

Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling

“…This is to avoid unnecessary computations on nonoverlapping image pairs. To achieve this, exterior orientation parameter (EOP)-based methods [14,20], the Delaunay triangulation-based method [13], and a graph-based method [21] can be used. From among them, we employed the EOP-based method using data obtained by a global positioning system/inertial navigation system mounted on a UAV.…”

“…Moussa and El-Sheimy [13] minimized the number of matching image pairs by structuring UAV images through Delaunay triangulation. Mehrdad et al [14] reduced tiepoint extraction regions using epipolar geometry established from initial camera parameters. Faraji et al [15] minimized the computation of tiepoint extraction using reference images in target areas.…”

“…Xu et al [9] minimized image distortions by making image transformations as close as possible to rigid transformations. Mehrdad et al [14] mitigated accumulated errors by estimating image transformations so that reprojection errors of tiepoints are minimized.…”