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.

Optimizing UAV Photography: Strategies in Flight Control and Image Capture

“…When using a drone for low-altitude aerial photography, a wide range of captured images are quickly obtained [25][26][27]. As the aerial photography height increases [28], the model recognition effect shows a downward trend.…”

MegaStitch: Robust Large-Scale Image Stitching

“…Related work on orthomosaic generation from largescale, geo-referenced images includes Mizotin et al [32], who proposed a voting scheme for shift and rotation estimation in the mosaicing of aerial images with low overlap and significant angle rotation, Xiang et al [33] who emphasized the importance of using GPS coordinates to find neighboring images to speed up the mosaicing by avoiding matching all possible image pairs, and Moussa et al [34] who proposed an iterative, region growing approach which combines using GPS coordinates with constrained Delaunay triangulation [35] to avoid exhaustive matching. Although some image mosaicing challenges were addressed in the later method, it accumulates small transformation errors in locations distal from the center (seed) of the mosaic.…”

MegaStitch: Robust Large Scale Image Stitching

“…Related work on orthomosaic generation from largescale, geo-referenced images includes Mizotin et al [23], who proposed a voting scheme for shift and rotation estimation in the mosaicing of aerial images with low overlap and significant angle rotation, Xiang et al [24] who emphasized the importance of using GPS coordinates to find neighboring images to speed up the mosaicing by avoiding matching all possible image pairs, and Moussa et al [25] who proposed an iterative, region growing approach which combines using GPS coordinates with constrained Delaunay triangulation [26] to avoid exhaustive matching. Although some image mosaicing challenges were addressed in the later method, it accumulates small transformation errors in locations distal from the center (seed) of the mosaic.…”