Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data

International Journal of Remote Sensing

Volumn 39, Issue 15-16, 2018, Pages 5236-5245

  Franklin, Steven E ^a   Ahmed, Oumer S ^b  

^a TRENT UNIVERSITY   (Canada)

^b Makivik Corporation   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ANTENNA ARRAYS; ARTIFICIAL INTELLIGENCE; DECISION TREES; FORESTRY; HARDWOODS; IMAGE ANALYSIS; UNMANNED AERIAL VEHICLES (UAV);

CLASSIFICATION ACCURACY; HIGH SPATIAL RESOLUTION; MACHINE LEARNING CLASSIFICATION; MULTI-SPECTRAL CAMERAS; OBJECT BASED IMAGE ANALYSIS; OBJECT-BASED ANALYSIS; RANDOM FOREST ALGORITHM; VARIABLE IMPORTANCES;

LEARNING SYSTEMS;

ACCURACY ASSESSMENT; CLASSIFICATION; DIGITAL IMAGE; IMAGE ANALYSIS; MACHINE LEARNING; SPATIAL RESOLUTION; SPECTRAL ANALYSIS; TREE;

CANADA; ONTARIO [CANADA];

ACER; BETULA PENDULA;

EID: 85027173616     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2017.1363442     Document Type: Article

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