A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach

Remote Sensing of Environment

Volumn 210, Issue , 2018, Pages 35-47

  Cai, Yaping ^a   Guan, Kaiyu ^a   Peng, Jian ^b   Wang, Shaowen ^a   Seifert, Christopher ^c   Wardlow, Brian ^d   Li, Zhan ^e  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b University of Illinois at Urbana Champaign   (United States)

^c STANFORD UNIVERSITY   (United States)

^d UNIVERSITY OF NEBRASKA LINCOLN   (United States)

^e UNIVERSITY OF MASSACHUSETTS BOSTON   (United States)

Author keywords

Crop type classification; Deep Neural Network (DNN); Machine learning; Phenology; Remote sensing

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOLOGY; COST EFFECTIVENESS; CROPS; DECISION MAKING; DEEP NEURAL NETWORKS; DISTRIBUTED COMPUTER SYSTEMS; ELECTRONIC TRADING; INFRARED DEVICES; LEARNING SYSTEMS; REMOTE SENSING; SUPPLY CHAINS; TIME SERIES;

CLASSIFICATION PERFORMANCE; COMPUTATIONAL EXPERIMENT; CROP TYPE CLASSIFICATION; HIGH PERFORMANCE COMPUTING; MACHINE LEARNING APPROACHES; PHENOLOGY; TIME SERIES INFORMATIONS; VISIBLE AND NEAR INFRARED;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CROP PERFORMANCE; DECISION MAKING; FIELD METHOD; LANDSAT; MACHINE LEARNING; METHODOLOGY; PHENOLOGY; SATELLITE DATA; SATELLITE IMAGERY; TIME SERIES ANALYSIS; VEGETATION CLASSIFICATION;

CORN BELT; UNITED STATES;

GLYCINE MAX; ZEA MAYS;

EID: 85043598532     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2018.02.045     Document Type: Article

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