Semi-supervised hyperspectral image classification using a new (soft) sparse multinomial logistic regression model

Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

Volumn , Issue , 2011, Pages

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

^a INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^b UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

Hyperspectral image classification; semi supervised learning; soft labels; sparse multinomial logistic regression; unlabeled training samples

Indexed keywords

AIRBORNE VISIBLE INFRARED IMAGING SPECTROMETER; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGE CLASSIFICATION; INDIANA; MULTINOMIAL LOGISTIC REGRESSION; MULTINOMIAL LOGISTIC REGRESSION MODELS; SEMI-SUPERVISED; SEMI-SUPERVISED ALGORITHM; SEMI-SUPERVISED CLASSIFICATION; SEMI-SUPERVISED LEARNING; STATE-OF-THE-ART PERFORMANCE; TRAINING SAMPLE; UNLABELED SAMPLES; UNLABELED TRAINING SAMPLES;

EVOLUTIONARY ALGORITHMS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; INDEPENDENT COMPONENT ANALYSIS; INFRARED IMAGING; LEARNING ALGORITHMS; REMOTE SENSING; SAMPLING; SIGNAL PROCESSING; SUPERVISED LEARNING;

REGRESSION ANALYSIS;

EID: 84255182494     PISSN: 21586276     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WHISPERS.2011.6080879     Document Type: Conference Paper

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