Low-cost orthographic imagery

Abstract

Commercial aerial imagery websites, such as Google Maps, MapQuest, Microsoft Virtual Earth, and Yahoo! Maps, provide high-seamless orthographic imagery for many populated areas, employing sophisticated equipment and proprietary image postprocessing pipelines. There are many areas of the world with poor coverage where locals might benefit from recent, high-resolution orthographic imagery, but which do not fit into the schedules and scaling model of the big sites.This paper describes MapStitcher, a system that orthorectifies and geographically registers imagery using only low-cost capturing equipment. MapStitcher combines manually-entered relationships between images and known ground references with a MOPs-based image-stitching technique that automatically discovers image-toimage relationships. Our image registration pipeline first extracts and matches feature points, then clusters images, then uses RANSAC-initialized bundle adjustment to simultaneously optimize all constraints over the entire image set. Simultaneous optimization balances the requirements of precise stitching and absolute placement accuracy. We used this technique to image a portion of the Skagit River Valley in the vicinity of the town of Concrete, WA (pop. 790) at 0.15 m/pixel. Our technique is more accurate than stitching followed by "rubber-sheeting" (deforming the stitched image into global coordinates), while it also requires less effort and produces a better-stitched composite than rubber-sheeting images separately.

Photogrammetric Analysis of Images Acquired by an UAV

“…A description for commercial software to create aerial maps is introduced in [6]. The system takes orthorectified and geographically registered imagery.…”

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

“…Many approaches to the problem have been presented in literature. Several works are based on GCPs, whose known positions on the terrain are matched with their coordinates in the UAV images in order to compute the transformation matrices mapping one image to the other [20–22]. However, this technique often requires user interaction for the accurate identification of GCPs on images, or it cannot be applied in areas where disruptive events, like flood fills or landslides, occurred.…”

“…The algorithm described in [26] quickly creates an initial mosaic of pseudo orthogonal images that is further refined, as more images are available over time, by optimizing a quality function, which involves visual constraints, in a short neighborhood of the camera position computed from UAV sensors. In order to produce orthomosaics, free of distortion also in areas with elevation variations and suitable for area or distance measurements, several approaches (as [20,22,27–29]) take into account a correct image orthorectification. It should be underlined that a possible drawback of hybrid approaches, which base image registration on both metadata and visual contents, is that image alignment is often an iterative approach, where each new image to be added to the mosaic is matched with a previously aligned image.…”

“…As a result, the concatenation of pairwise image transformations can lead to accumulated errors. In order to solve this problem, several works (as [22,30,31]) proposed to use a bundle adjustment, which simultaneously refines all the transformation parameters according to an optimality criterion based again on the correspondences between corresponding features in the different images.…”

Incremental, orthorectified and loop-independent mosaicking of aerial images taken by micro UAVs

“…Based on these parameters a variety of approaches are proposed for mosaicking of images taken from UAVs. Automatic mosaicking by 3D-reconstruction and epipolar geometry [8], [14], combining global positioning system (GPS), inertial measurement unit (IMU) and video sensors for external distortion correction and geo-referencing [3], waveletbased stitching [17], triangulated irregular network registration and perspective correction [13] and high altitude imaging and mosaicking [19], [5], [10], [13] are some of those examples. Schultz et al [11] use a digital elevation model to mosaic images taken from an airplane.…”