An assessment of the effectiveness of a random forest classifier for land-cover classification

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 67, Issue 1, 2012, Pages 93-104

  Rodriguez Galiano, V F ^a   Ghimire, B ^b   Rogan, J ^b   Chica Olmo, M ^a   Rigol Sanchez, J P ^c  

^a UNIVERSITY OF GRANADA   (Spain)

^b CLARK UNIVERSITY   (United States)

^c UNIVERSITY OF JAÉN   (Spain)

Author keywords

Classification; Land cover; Landsat Thematic Mapper; Machine learning; Random forest; Remote sensing

Indexed keywords

ACCURATE MAPPING; AUXILIARY VARIABLES; BLACK BOXES; CLASSIFICATION ACCURACY; DATA SET SIZE; DIGITAL TERRAIN MODEL; KAPPA INDEX; KAPPA VALUES; LAND COVER; LAND COVER CLASSIFICATION; LANDSAT THEMATIC MAPPER; LANDSAT-5; MACHINE-LEARNING; MAPPING ACCURACY; MCNEMAR; MISSING VALUES; NOISE ADDITION; NON-PARAMETRIC; PATTERN RECOGNITION TECHNIQUES; RANDOM FOREST CLASSIFIER; RANDOM FORESTS; REMOTELY SENSED DATA; ROBUST CLASSIFICATION; SINGLE DECISION; SURVIVAL ANALYSIS; THEMATIC MAPPERS;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; DATA REDUCTION; DECISION TREES; FORESTRY; LANDFORMS; LEARNING SYSTEMS; PATTERN RECOGNITION; REMOTE SENSING;

MAPPING;

ACCURACY ASSESSMENT; ALGORITHM; DIGITAL TERRAIN MODEL; IMAGE CLASSIFICATION; LAND COVER; LAND USE CHANGE; LANDSAT THEMATIC MAPPER; LEARNING; PERFORMANCE ASSESSMENT; REMOTE SENSING; SATELLITE DATA;

ALGORITHMS; CLASSIFICATION; DATA PROCESSING; FORESTRY; MAPPING; REMOTE SENSING;

SPAIN;

EID: 84855970934     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2011.11.002     Document Type: Article

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