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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 19, Iss. 21 — Oct. 10, 2011
  • pp: 20916–20929

Object level HSI-LIDAR data fusion for automated detection of difficult targets

A. V. Kanaev, B. J. Daniel, J. G. Neumann, A. M. Kim, and K. R. Lee  »View Author Affiliations


Optics Express, Vol. 19, Issue 21, pp. 20916-20929 (2011)
http://dx.doi.org/10.1364/OE.19.020916


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Abstract

Data fusion from disparate sensors significantly improves automated man-made target detection performance compared to that of just an individual sensor. In particular, it can solve hyperspectral imagery (HSI) detection problems pertaining to low-radiance man-made objects and objects in shadows. We present an algorithm that fuses HSI and LIDAR data for automated detection of man-made objects. LIDAR is used to define a set of potential targets based on physical dimensions, and HSI is then used to discriminate between man-made and natural objects. The discrimination technique is a novel HSI detection concept that uses an HSI detection score localization metric capable of distinguishing between wide-area score distributions inherent to natural objects and highly localized score distributions indicative of man-made targets. A typical man-made localization score was found to be around 0.5 compared to natural background typical localization scores being less than 0.1.

© 2011 OSA

OCIS Codes
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(280.4788) Remote sensing and sensors : Optical sensing and sensors

ToC Category:
Remote Sensing

History
Original Manuscript: June 2, 2011
Revised Manuscript: September 9, 2011
Manuscript Accepted: September 15, 2011
Published: October 6, 2011

Citation
A. V. Kanaev, B. J. Daniel, J. G. Neumann, A. M. Kim, and K. R. Lee, "Object level HSI-LIDAR data fusion for automated detection of difficult targets," Opt. Express 19, 20916-20929 (2011)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-21-20916


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