Variables effecting photomosaic reconstruction and ortho-rectification from aerial survey datasets

Unmanned aerial vehicles now make it possible to obtain high quality aerial imagery at a low cost, but processing those images into a single, useful entity is neither simple nor seamless. Specifically, there are factors that must be addressed when merging multiple images into a single coherent one. While ortho-rectification can be done, it tends to be expensive and time consuming. Image stitching offers a more economical, low-tech approach. However direct application tends to fail for low-elevation imagery due to one or more factors including insufficient keypoints, parallax issues, and homogeneity of the surveyed area. This paper discusses these problems and possible solutions when using techniques such as image stitching and structure from motion for generating ortho-rectified imagery. These are presented in terms of actual Irish projects including the Boland's Mills building in Dublin's city centre, the Kilmoon Cross Farm, and the Richview buildings on the University College Dublin campus. Implications for various Irish industries are explained in terms of both urban and rural projects. 1 INTRODUCTION Ortho-rectified imagery provides a useful tool to civil engineers for planning, boundary mapping, and surveying. It can furnish information sufficiently detailed to derive measured drawings, with the added benefit of containing visual information not typically captured in standard line drawings. However, traditional ortho-rectification is a slow and labour-intensive process. Once aerial imagery has been gathered, a surveyor must collect ground control points (GCP) visible in the overhead images. This involves either using a global positioning system (GPS) with a real time kinematics system or a total station throughout the survey area. While both can provide excellent accuracy, the costs to do so are high. Alternatively, there are automatic techniques for matching and merging images. The two primary approaches to automatically generating orthomosaics are photo-stitching and Structure From Motion (SFM). Photo-stitching combines multiple images with overlapping fields of a view to create a high resolution image. While the process is very fast, lighting variance and image alignment can be problematic. In contrast, the newer SFM approach operates on a principle similar to stereo vision to generate a three-dimensional (3D) scene model. Although SFM is much more computationally expensive, it generates a 3D model, which can be used to generate an ortho-rectified image; photo-stitching cannot. UAV photography differs from traditional satellite or aerial imagery in that it is usually captured at a much lower height: normally between 30 m and 100 m. Accordingly, specific factors …