Classifying complex mountainous forests with L-Band SAR and landsat data integration: A comparison among different machine learning methods in the Hyrcanian forest

Remote Sensing

Volumn 6, Issue 5, 2014, Pages 3624-3647

  Attarchi, Sara ^a   Gloaguen, Richard ^a,b  

^a TU BERGAKADEMIE FREIBERG   (Germany)

^b INSTITUTE OF ION BEAM PHYSICS AND MATERIALS RESEARCH   (Germany)

Author keywords

ALOS PALSAR; Hyrcanian mountainous forest; Iran; L band; Landsat; Maximum likelihood classification; Neural networks; Random forest; Support vector machines; Topographic effects

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; DECISION TREES; LANDFORMS; NEURAL NETWORKS;

ALOS/PALSAR; HYRCANIAN MOUNTAINOUS FOREST; IRAN; L-BAND; LANDSAT; MAXIMUM LIKELIHOOD CLASSIFICATIONS; RANDOM FORESTS; TOPOGRAPHIC EFFECTS;

SUPPORT VECTOR MACHINES;

EID: 84901479073     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs6053624     Document Type: Article

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