Joint Deep Learning for land cover and land use classification

Remote Sensing of Environment

Volumn 221, Issue , 2019, Pages 173-187

  Zhang, Ce ^a   Sargent, Isabel ^b   Pan, Xin ^c,d   Li, Huapeng ^d   Gardiner, Andy ^b   Hare, Jonathon ^e   Atkinson, Peter M ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b ORDNANCE SURVEY   (United Kingdom)

^c CHANGCHUN INSTITUTE OF TECHNOLOGY   (China)

^d NORTHEAST INSTITUTE OF GEOGRAPHY AND AGRICULTURAL ECOLOGY   (China)

^e UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Convolutional neural network; Land cover and land use classification; Multilayer perceptron; Object based CNN; VFSR remotely sensed imagery

Indexed keywords

AERIAL PHOTOGRAPHY; ANTENNAS; CLASSIFICATION (OF INFORMATION); CONVOLUTION; ITERATIVE METHODS; LAND USE; MARKOV PROCESSES; MULTILAYER NEURAL NETWORKS; MULTILAYERS; NEURAL NETWORKS; REMOTE SENSING;

CLASSIFICATION ACCURACY; CONVOLUTIONAL NEURAL NETWORK; CONVOLUTIONAL NEURAL NETWORKS (CNN); LANDUSE CLASSIFICATIONS; OBJECT BASED; REMOTE SENSING CLASSIFICATION; REMOTELY SENSED IMAGERY; URBAN AND SUBURBAN AREAS;

DEEP LEARNING;

AERIAL PHOTOGRAPHY; ALGORITHM; ARTIFICIAL NEURAL NETWORK; LAND COVER; LAND USE CHANGE; REMOTE SENSING; SATELLITE IMAGERY; SPATIAL RESOLUTION; SUBURBAN AREA; URBAN AREA;

ENGLAND; MANCHESTER [ENGLAND]; SOUTHAMPTON [ENGLAND]; UNITED KINGDOM;

EID: 85056770380     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2018.11.014     Document Type: Article

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