Hyperspectral image segmentation using a new bayesian approach with active learning

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 10 PART 2, 2011, Pages 3947-3960

  Li, Jun ^a   Bioucas Dias, José M ^b   Plaza, Antonio ^a  

^a UNIVERSITY OF EXTREMADURA   (Spain)

^b INSTITUTO DE TELECOMUNICAÇÕES   (Portugal)

Author keywords

Active learning; graph cuts; hyperspectral image segmentation; ill posed problems; integer optimization; mutual information (MI); sparse multinomial logistic regression (MLR)

Indexed keywords

ACTIVE LEARNING; GRAPH CUT; HYPER-SPECTRAL IMAGES; ILL-POSED PROBLEMS; INTEGER OPTIMIZATION; MULTINOMIAL LOGISTIC REGRESSION; MUTUAL INFORMATIONS;

ALGORITHMS; BAYESIAN NETWORKS; INTEGER PROGRAMMING; LAGRANGE MULTIPLIERS; OPTIMIZATION; PROBABILITY DISTRIBUTIONS; REGRESSION ANALYSIS;

IMAGE SEGMENTATION;

ALGORITHM; ARTIFICIAL INTELLIGENCE; BAYESIAN ANALYSIS; DATA ACQUISITION; DATA SET; IMAGE ANALYSIS; IMAGE CLASSIFICATION; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS; SAMPLING; SEGMENTATION;

EID: 80053562930     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2011.2128330     Document Type: Article

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