Research Papers

Spin-image target detection algorithm applied to low density 3D point clouds

[+] Author Affiliations
Michael S. Foster, John R. Schott, David Messinger

Digital Imaging and Remote Sensing Laboratory, Rochester Institute of Technology, Chester F. Carlson Center for Imaging Science, Rochester, NY 14623

J. Appl. Remote Sens. 2(1), 023539 (September 29, 2008). doi:10.1117/1.3002398
History: Received November 15, 2007; Revised July 28, 2008; Accepted September 15, 2008; September 29, 2008; Online September 29, 2008
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Abstract

Target detection in 3-dimensional (3D), irregular point cloud data sets is an emerging field of study among the remote sensing community. Airborne topographic light detection and ranging (i.e., Lidar) systems are capable of scanning areas with single-pass post spacings on the order 0.2 m. Unfortunately, many of the current spatial search algorithms require higher spatial resolutions on a target object in order to achieve robust detection performance with low false alarm levels. This paper explores the application of Johnson's spin-image surface matching algorithm to low density point clouds for the purpose of providing a preliminary spatial cue to a secondary sensor. In the event that this sensor is an imaging device, a method is presented for transforming 3D points into a fixed, gridded coordinate system relative to the second sensor.

© 2008 Society of Photo-Optical Instrumentation Engineers

Citation

Michael S. Foster ; John R. Schott and David Messinger
"Spin-image target detection algorithm applied to low density 3D point clouds", J. Appl. Remote Sens. 2(1), 023539 (September 29, 2008). ; http://dx.doi.org/10.1117/1.3002398


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