NDLI: Enhancing classification accuracy via registration of discrete return LiDAR and aerial imagery using the Levenberg-Marquardt nonlinear optimization method

Content Provider	IEEE Xplore Digital Library
Author	Bandyopadhyay, M. van Aardt, J.A.N. Cawse-Nicholson, K.
Copyright Year	2013
Description	Author affiliation: Chester F. Carlson Center for Imaging Sci., Rochester Inst. of Technol., Rochester, NY, USA (Bandyopadhyay, M.; van Aardt, J.A.N.; Cawse-Nicholson, K.)
Abstract	Description and quantification of a landscape or scene can be achieved by assessing its spectral and structural properties. Fusion of spectral information from aerial imagery and 3-D structural information from LiDAR point clouds allows us to integrate these two complementary characteristics. However, in any fusion method, alignment of data sets is crucial. We registered aerial color (RGB) imagery with LiDAR data by computing a homography matrix(H), using the Levenberg-Marquardt nonlinear optimization method. The root mean square error (RMSE) of registration was less than 0.5 m. The overall classification accuracy of our fusion based object extraction algorithm was also increased from 85% to 90%, when applied to a pre and post registered data set, respectively. In this paper, two different regions were selected to demonstrate the registration method and improved classification results.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	3411
Ending Page	3414
File Size	911298
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laser radar Buildings Accuracy Three-dimensional displays Image color analysis Solid modeling Vectors Registration Homography Levenberg-Marquardt Nonlinear Optimization
Content Type	Text
Resource Type	Article

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