By Topic

Automated cross-sensor registration, orthorectification and geopositioning using LIDAR digital elevation models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mark D. Pritt ; Lockheed Martin, 700 N. Frederick Ave., Gaithersburg, MD 20879 ; Michael Gribbons ; Kevin LaTourette

Cross-sensor image registration, orthorectification, and geopositioning of imagery are well-known problems whose solutions are difficult, if not impossible, to automate. Registration of radar to optical imagery typically requires a manual solution, as does the registration of imagery over rugged terrain or urban areas, where foreshortening and layover present formidable obstacles to successful automation. We have developed an automated solution that is based on the registration of imagery to high-precision digital elevation models (DEMs) derived from Lidar data. The key idea is the generation of a simulated image using Lidar data, the image camera model and the illumination conditions. The simulated image is then registered to the actual image with normalized cross-correlation methods. The result is an effective and completely automated technique for registering imagery to DEMs. It has been shown to work with BuckEye Lidar, ALIRT Lidar, commercial satellite imagery and commercial synthetic aperture radar imagery over diverse terrain types, including mountains, cities, and forests. It provides an automated solution to many difficult geospatial problems, including cross-sensor registration of radar and optical imagery, image registration over rugged terrain, geopositioning of imagery and orthorectification. Its use of Lidar enables it to handle three-dimensional features that are foreshortened or laid over in different directions. Its use of simulated imagery enables it to bypass the problem of disparate features in cross-sensor registration. Statistical analyses of the registration accuracy are presented along with results on commercial satellite imagery and Lidar data over Iraq, Afghanistan, Haiti and the U.S.

Published in:

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)

Date of Conference:

13-15 Oct. 2010