Land-mine detection with ground-penetrating radar using multistream discrete hidden Markov models

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 6 PART 1, 2011, Pages 2080-2099

  Missaoui, Oualid ^a   Frigui, Hichem ^b   Gader, Paul ^c  

^a Pipeline Financial Group Inc   (United States)

^b University of Louisville   (United States)

^c UNIVERSITY OF FLORIDA   (United States)

Author keywords

BaumWelch (BW); discrete hidden Markov model (DHMM); discriminative training; generalized probabilistic descent (GPD); maximum likelihood; minimum classification error (MCE); multistream DHMM (MSDHMM); stream weighting

Indexed keywords

BAUM-WELCH; DISCRETE HIDDEN MARKOV MODEL (DHMM); DISCRIMINATIVE TRAINING; GENERALIZED PROBABILISTIC DESCENTS; MINIMUM CLASSIFICATION ERROR (MCE); MULTI-STREAM; STREAM WEIGHTING;

FEATURE EXTRACTION; GEOLOGICAL SURVEYS; GROUND PENETRATING RADAR SYSTEMS; LEARNING ALGORITHMS; MAXIMUM LIKELIHOOD; OBJECT RECOGNITION; PROBABILITY; RADAR; SPEECH RECOGNITION;

HIDDEN MARKOV MODELS;

ALGORITHM; DATA SET; DISCRETE ELEMENT METHOD; ERROR ANALYSIS; GROUND PENETRATING RADAR; LAND COVER; MARKOV CHAIN; MAXIMUM LIKELIHOOD ANALYSIS; MINE; PROBABILITY;

EID: 79957645046     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2090886     Document Type: Article

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