Paddy-Rice Phenology Classification Based on Machine-Learning Methods Using Multitemporal Co-Polar X-Band SAR Images

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

Volumn 9, Issue 6, 2016, Pages 2509-2519

  Kucuk, Caglar ^a   Taskin, Gulsen ^a   Erten, Esra ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Agriculture; Classification; Support vector machines (SVM); Synthetic aperture radar (SAR); TerraSAR X

Indexed keywords

AGRICULTURAL MACHINERY; AGRICULTURE; ARTIFICIAL INTELLIGENCE; BIOLOGY; CLASSIFICATION (OF INFORMATION); CROPS; DECISION TREES; LEARNING SYSTEMS; MAPS; NEAREST NEIGHBOR SEARCH; RADAR; RADAR IMAGING; SATELLITES; SPACE-BASED RADAR; SUPPORT VECTOR MACHINES; SYNTHETIC APERTURE RADAR; WEATHER SATELLITES;

AGRICULTURAL AREAS; CLASSIFICATION ALGORITHM; HIGH TEMPORAL RESOLUTION; K NEAREST NEIGHBOR (KNN); MORPHOLOGICAL CHANGES; POLARIMETRIC FEATURES; SPACE-BASED SENSORS; TERRASAR-X;

IMAGE CLASSIFICATION;

AGRICULTURE; IMAGE CLASSIFICATION; MACHINE LEARNING; PADDY FIELD; PHENOLOGY; RADAR IMAGERY; RICE; SUPPORT VECTOR MACHINE; SYNTHETIC APERTURE RADAR; TERRASAR-X;

SPAIN; TURKEY;

EID: 84978194787     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2016.2547843     Document Type: Article

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