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Mine Discrimination Using Multispectral Imagery With Feedforward Neural Networks

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dc.contributor.author Daud, T. en_US
dc.contributor.author Duong, T. en_US
dc.contributor.author Langenbacher, H. en_US
dc.contributor.author Tsu, H. en_US
dc.contributor.author Thakoor, A. en_US
dc.date.accessioned 2004-10-02T18:03:10Z
dc.date.available 2004-10-02T18:03:10Z
dc.date.issued 1995-04 en_US
dc.identifier.citation Orlando, FL en_US
dc.identifier.clearanceno 95-0508 en_US
dc.identifier.uri http://hdl.handle.net/2014/30018
dc.description.abstract Simulated mine detection was performed on a polarimetric hyperspectral imaging dataset collected by using an acousto-optic tunable filter camera. A feedforward artificial neural network was programmed to recognize predefined spectral "templates." The simulation results are provided along with the preprocessing steps and window sizes leading to mine detection without false alarms. en_US
dc.format.extent 1110792 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject.other polarimetric hyperspectral imaging en_US
dc.title Mine Discrimination Using Multispectral Imagery With Feedforward Neural Networks en_US


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