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

Download
Author
Kanaev, A.V.
Daniel, B.J.
Neumann, J.G.
Kim, A.M.
Lee, K.R.
Date
2011-10Metadata
Show full item recordAbstract
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.Collections
Related items
Showing items related by title, author, creator and subject.
-
Event detection challenges, methods, and applications in natural and artificial systems
Kerman, Mitchell C.; Jiang, Wei; Blumberg, Alan F.; Buttrey, Samuel E (2009-06);A system is a combination of elements whose collaborative actions produce results generally not attainable by the elements acting alone, and an event is a significant occurrence or large-scale activity that is unusual ... -
An effective noise filtering method for mine detection
Hong, Bryan Y. (Monterey, California. Naval Postgraduate School, 2011-09);Automatic detection of sea mines in coastal regions is difficult due to highly varying sea bottom conditions present in the underwater environment. Detection systems must be able to discriminate objects that vary in size, ... -
Seeing eye drones: how the DoD can transform CBRN and disaster response in the homeland
Jonkey, Matthew J. (Monterey, California: Naval Postgraduate School, 2016-12);The threat of chemical, biological, radiological, and nuclear (CBRN) disasters is one of the most dangerous threats to the homeland. The United States has an opportunity to harness emerging technology to increase responder ...