Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover

Remote Sensing of Environment

Volumn 205, Issue , 2018, Pages 253-275

  Goldblatt, Ran ^a   Stuhlmacher, Michelle F ^b   Tellman, Beth ^b   Clinton, Nicholas ^c   Hanson, Gordon ^a   Georgescu, Matei ^b   Wang, Chuyuan ^b   Serrano Candela, Fidel ^d   Khandelwal, Amit K ^e   Cheng, Wan Hwa ^b   Balling, Robert C ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

^c GOOGLE INC   (United States)

^d UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

^e The Sanford C Bernstein Co Center for Leadership and Ethics   (United States)

Author keywords

Built up land cover; Google Earth Engine; Image classification; Nighttime light; Urbanization

Indexed keywords

DATA FUSION; ENGINES; IMAGE CLASSIFICATION; LEARNING SYSTEMS; PIXELS;

BUILD-UP LAND COVER; GOOGLE EARTH ENGINE; GOOGLE EARTHS; IMAGES CLASSIFICATION; LAND COVER; LANDSAT; NIGHT TIME LIGHTS; SCIENTIFIC PROGRESS; URBAN LAND-COVER; URBANIZATION;

LANDSAT;

FUNCTIONAL ROLE; IMAGE CLASSIFICATION; LAND COVER; LANDSAT; MACHINE LEARNING; NIGHTGLOW; PIXEL; SATELLITE DATA; SOFTWARE; SUSTAINABILITY; URBAN AREA; URBANIZATION;

INDIA; MEXICO [NORTH AMERICA];

EID: 85037545229     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.11.026     Document Type: Article

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