SAR image classification through information-theoretic textural features, MRF segmentation, and object-oriented learning vector quantization

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 7, Issue 4, 2014, Pages 1116-1126

  Delia, Ciro ^a   Ruscino, Simona ^a   Abbate, Maurizio ^a   Aiazzi, Bruno ^b   Baronti, Stefano ^b   Alparone, Luciano ^b,c  

^a UNIVERSITY OF CASSINO AND SOUTHERN LAZIO   (Italy)

^b IFAC CNR   (Italy)

^c UNIVERSITY OF FLORENCE   (Italy)

Author keywords

Artificial neural network (ANN); learning vector quantization (LVQ); segmentation; synthetic aperture radar (SAR) images; textural features; thematic classification

Indexed keywords

CLASSIFICATION (OF INFORMATION); GEOMETRICAL OPTICS; IMAGE CLASSIFICATION; INFORMATION THEORY; MARKOV PROCESSES; NEURAL NETWORKS; SYNTHETIC APERTURE RADAR; VECTOR QUANTIZATION;

LEARNING VECTOR QUANTIZATION; OBJECT ORIENTED APPROACH; OBJECT ORIENTED CLASSIFICATION; SAR IMAGE CLASSIFICATIONS; SEGMENTATION ALGORITHMS; SYNTHETIC APERTURE RADAR (SAR) IMAGES; TEXTURAL FEATURE; THEMATIC CLASSIFICATION;

IMAGE SEGMENTATION;

ARTIFICIAL NEURAL NETWORK; IMAGE CLASSIFICATION; MARKOV CHAIN; PATTERN RECOGNITION; RADAR IMAGERY; SEGMENTATION; SYNTHETIC APERTURE RADAR;

EID: 84899978984     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2304700     Document Type: Article

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