Mapping urban areas in China using multisource data with a novel ensemble SVM method

IEEE Transactions on Geoscience and Remote Sensing

Volumn 56, Issue 8, 2018, Pages 4258-4273

  Huang, Xin ^a   Hu, Ting ^a   Li, Jiayi ^a   Wang, Qing ^a   Benediktsson, Jón Atli ^b  

^a WUHAN UNIVERSITY   (China)

^b UNIVERSITY OF ICELAND   (Iceland)

Author keywords

Data fusion; ensemble support vector machine (SVM); socioeconomic data; training samples; urban areas; urban mapping

Indexed keywords

BIODIVERSITY; CONSERVATION; COST EFFECTIVENESS; DATA FUSION; DATA INTEGRATION; DECISION MAKING; IMAGE RETRIEVAL; MAPPING; PERSONNEL TRAINING; RADIOMETERS; REMOTE SENSING; SAMPLING; SUPPORT VECTOR MACHINES; WAGES;

ECONOMIC INDICATORS; LAND SURFACE TEMPERATURE; SOCIO-ECONOMIC DATA; TRAINING SAMPLE; URBAN AREAS; URBAN MAPPING;

ATMOSPHERIC TEMPERATURE;

BIODIVERSITY; DECISION MAKING; ENVIRONMENTAL MONITORING; MAPPING METHOD; MODIS; NUMERICAL METHOD; NUMERICAL MODEL; REMOTE SENSING; SAMPLING; SATELLITE DATA; SOCIOECONOMIC IMPACT; SUPERVISED CLASSIFICATION; SUPPORT VECTOR MACHINE; URBAN AREA;

CHINA;

EID: 85042848598     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2018.2805829     Document Type: Article

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