Feature-Based Georegistration of Aerial Images

摘要

Georegistration is the alignment of an observed image with a geodetically cali-brated reference image. Such alignment allows each observed image pixel to inheritthe coordinates and elevation of the reference pixel it is aligned to. Accurate georeg-istration of video has far-reaching implications for the future of automation. An agent(such as a robot or a UAV), equipped with the ability to precisely assign geodetic co-ordinates to objects or artifacts within its field of view, can be an indispensable toolin applications as diverse as planetary exploration and automated vacuum cleaners.In this chapter, we present an algorithm for the automated registration of aerial videoframes to a wide area reference image. The data typically available in this applicationare the reference imagery, the video imagery and the telemetry information.The reference imagery is usually a wide area, high-resolution ortho-image. Eachpixel in the reference image has a longitude, latitude and elevation associated with it(in the form of a DEM -Digital Elevation Map). Since the reference image is usuallydated by the time it is used for georegistration, it contains significant dissimilaritieswith respect to the aerial video data. The aerial video data is captured from a cam-era mounted on an aircraft. The orientation and position of the camera are recorded,per-frame, in the telemetry information. Since each frame has this telemetry informa-tion associated with it, georegistration would seem to be a trivial task of projecting theimage onto the reference image coordinates. Unfortunately, mechanical noise causesfluctuations in the telemetry measurements, which in turn causes significant projec-tion errors, sometimes up to hundreds of pixels. Thus while the telemetry informationprovides coarse alignment of the video frame, georegistration techniques are requiredto obtain accurate pixel-wise calibration of each aerial image pixel. In this chapter,we use the telemetry information to orthorectify the aerial images, to bring both im-ageries into a common projection space, and then apply our registration technique toachieve accurate alignment. The challenge in georegistration lies in the stark differ-ences between the video and reference data. While the difference of projection view isaccounted for by orthorectification, four types of data distortions are still encountered:(1) Sensor Noise in the form of erroneous Telemetry Data, (2) Lighting and Atmo-spheric Changes, (3) Blurring, (4) Object Changes in the form of forest growths or