A Fast Approach for Stitching of Aerial Images

Abstract

ABSTRACT:The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image's coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.

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.…”

“…However, this may not take into account the imaging characteristics of small UAVs that are sensitive to environmental changes. Accordingly, the existing methods may determine a relatively tilted image as a mosaic plane [9,13]. This concern was actually realized, as shown in Figure 13a,b.…”

“…Enhancing mosaicking speed was tried by reducing the processing time for tiepoint extraction, which requires the greatest amount of computation. 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.…”

“…On the other hand, enhancing mosaicking accuracy was tried by minimizing the accumulated errors and distortions that are likely to occur in image transformation estimation. Moussa and El-Sheimy [13] minimized the accumulated errors using proximity among the images. Xu et al [9] minimized image distortions by making image transformations as close as possible to rigid transformations.…”

Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

“…Furthermore, the generation of orthomosaics is time-consuming and cannot satisfy the demand of real-time crop growth monitoring. With the purpose of real-time applications, previous researchers found that mosaicking with a proper strategy is very efficient in crop growth monitoring [65,66]. Moreover, image stitching is recommended for a small field, when the terrain is sufficiently flat [67].…”