A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 135, Issue , 2018, Pages 173-188

  Houborg, Rasmus ^a,b   McCabe, Matthew F ^a  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^b SOUTH DAKOTA STATE UNIVERSITY   (United States)

Author keywords

Cubist; Hybrid; LAI; Machine learning; Random forests; REGFLEC

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; INFRARED DEVICES; LEARNING ALGORITHMS; LEARNING SYSTEMS; REGRESSION ANALYSIS; VEGETATION;

CUBIST; HYBRID; HYBRID TRAINING APPROACHES; LEAF AREA INDEX; MACHINE-LEARNING; PREDICTOR VARIABLES; RANDOM FORESTS; REGFLEC; REGRESSION MODELLING; TRAINING DATASET;

DECISION TREES;

ALGORITHM; DATA MINING; DATA SET; DECISION SUPPORT SYSTEM; LANDSAT; LEAF AREA INDEX; MACHINE LEARNING; MODEL VALIDATION; RAPIDEYE; VEGETATION INDEX;

EID: 85036627061     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2017.10.004     Document Type: Article

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