Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory

Remote Sensing

Volumn 10, Issue 4, 2018, Pages

  Berhane, Tedros M ^a   Lane, Charles R ^b   Wu, Qiusheng ^c   Autrey, Bradley C ^b   Anenkhonov, Oleg A ^d   Chepinoga, Victor V ^e,f   Liu, Hongxing ^g  

^a PEGASUS TECHNICAL SERVICES INC   (United States)

^b U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^c BINGHAMTON UNIVERSITY   (United States)

^d INSTITUTE OF GENERAL AND EXPERIMENTAL BIOLOGY   (Russian Federation)

^e V B SOCHAVA INSTITUTE OF GEOGRAPHY   (Russian Federation)

^f IRKUTSK STATE UNIVERSITY   (Russian Federation)

^g UNIVERSITY OF CINCINNATI   (United States)

Author keywords

Freshwater wetland; Lake Baikal; Methodological comparison; Selenga River Delta; WorldView 2

Indexed keywords

DECISION TREES; IMAGE CLASSIFICATION; IMAGE ENHANCEMENT; LAKES; LEARNING ALGORITHMS; LEARNING SYSTEMS; REMOTE SENSING; VEGETATION; WETLANDS;

FRESHWATER WETLANDS; LAKE BAIKAL; METHODOLOGICAL COMPARISON; RIVER DELTAS; WORLDVIEW-2;

SATELLITE IMAGERY;

EID: 85045960898     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10040580     Document Type: Article

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