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

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Author
Kanaev, A.V.
Daniel, B.J.
Neumann, J.G.
Kim, A.M.
Lee, K.R.
Date
2011-10
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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.
Description
Optics Express, Volume 19, No. 21, pp. 20916-20929 (10 October 2011)
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
URI
http://hdl.handle.net/10945/44131
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