Mapping paddy rice fields by applying machine learning algorithms to multi-temporal sentinel-1A and landsat data

International Journal of Remote Sensing

Volumn 39, Issue 4, 2018, Pages 1042-1067

  Onojeghuo, Alex O ^a   Blackburn, George A ^a,b   Wang, Qunming ^c   Atkinson, Peter M ^c,d,e   Kindred, Daniel ^f   Miao, Yuxin ^g  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^c LANCASTER UNIVERSITY   (United Kingdom)

^d UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^e QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^f ADAS BOXWORTH   (United Kingdom)

^g CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CROPS; CULTIVATION; DECISION TREES; IMAGE RESOLUTION; POLARIZATION; SPACE-BASED RADAR; SUPPORT VECTOR MACHINES; SYNTHETIC APERTURE RADAR;

CLASSIFICATION ACCURACY; ILLUMINATION CONDITIONS; MACHINE LEARNING CLASSIFICATION; METHODOLOGICAL FRAMEWORKS; NORMALIZED DIFFERENCE VEGETATION INDEX DATUM; POLARIZATION CHANNELS; SATELLITE SENSOR DATA; TEMPORAL RESOLUTION;

LEARNING ALGORITHMS;

ALGORITHM; LANDSAT; MACHINE LEARNING; MAPPING; NDVI; PADDY FIELD; SATELLITE DATA; SENTINEL; SPATIAL DISTRIBUTION;

CHINA; HEILONGJIANG; SANJIANG PLAIN;

EID: 85047778316     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2017.1395969     Document Type: Article

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