Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery

Sensors (Switzerland)

Volumn 18, Issue 1, 2018, Pages

  Noi, Phan Thanh ^a,b   Kappas, Martin ^a  

^a UNIVERSITY OF GÖTTINGEN   (Germany)

^b VIETNAM NATIONAL UNIVERSITY OF AGRICULTURE   (Viet Nam)

Author keywords

Classification algorithms; K nearest neighbor (knn); Random forest (RF); Sentinel 2; Support vector machine (svm); Training sample size

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; LAND USE; LEARNING ALGORITHMS; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; PIXELS; REMOTE SENSING; SAMPLING; SUPPORT VECTOR MACHINES;

CLASSIFICATION ALGORITHM; K NEAREST NEIGHBOR (KNN); RANDOM FORESTS; SENTINEL-2; TRAINING SAMPLE;

IMAGE CLASSIFICATION;

EID: 85039732569     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s18010018     Document Type: Article

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