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dc.contributor.authorParsons, Olgaen_US
dc.contributor.authorCarpenter, Gailen_US
dc.date.accessioned2011-11-14T19:03:39Z
dc.date.available2011-11-14T19:03:39Z
dc.date.issued2002-09en_US
dc.identifier.urihttps://hdl.handle.net/2144/2299
dc.description.abstractThe Sensor Exploitation Group of MIT Lincoln Laboratory incorporated an early version of the ARTMAP neural network as the recognition engine of a hierarchical system for fusion and data mining of registered geospatial images. The Lincoln Lab system has been successfully fielded, but is limited to target I non-target identifications and does not produce whole maps. Procedures defined here extend these capabilities by means of a mapping method that learns to identify and distribute arbitrarily many target classes. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of canonical algorithms and a benchmark testbed has enabled the evaluation of candidate recognition networks as well as pre- and post-processing and feature selection options. The resulting mapping methodology sets a standard for a variety of spatial data mining tasks. In particular, training pixels are drawn from a region that is spatially distinct from the mapped region, which could feature an output class mix that is substantially different from that of the training set. The system recognition component, default ARTMAP, with its fully specified set of canonical parameter values, has become the a priori system of choice among this family of neural networks for a wide variety of applications.en_US
dc.description.sponsorshipAir Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624)en_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-2002-011en_US
dc.rightsCopyright 2002 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectARTMAPen_US
dc.subjectAdaptive Resonance Theory (ART)en_US
dc.subjectInformation fusionen_US
dc.subjectData miningen_US
dc.subjectRemote sensingen_US
dc.subjectMappingen_US
dc.subjectImage analysisen_US
dc.subjectPattern recognitionen_US
dc.titleARTMAP Neural Networks for Information Fusion and Data Mining: Map Production and Target Recognition Methodologiesen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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