Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery

Remote Sensing of Environment

Volumn 214, Issue , 2018, Pages 73-86

  Huang, Bo ^a   Zhao, Bei ^a   Song, Yimeng ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Classification; Deep convolutional neural networks; High spatial resolution; Land use mapping; Multispectral remote sensing image; Skeleton extraction; Street block; Transfer learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; DEEP NEURAL NETWORKS; IMAGE PROCESSING; IMAGE RESOLUTION; LAND USE; MUSCULOSKELETAL SYSTEM; NEURAL NETWORKS; REMOTE SENSING; URBAN PLANNING;

DEEP CONVOLUTIONAL NEURAL NETWORKS; HIGH SPATIAL RESOLUTION; LAND-USE MAPPINGS; MULTISPECTRAL REMOTE SENSING IMAGE; SKELETON EXTRACTION; TRANSFER LEARNING;

MAPPING;

ARTIFICIAL NEURAL NETWORK; DECOMPOSITION ANALYSIS; EXTRACTION METHOD; GEOLOGICAL MAPPING; IMAGE CLASSIFICATION; IMAGE PROCESSING; LAND MANAGEMENT; LAND USE PLANNING; MULTISPECTRAL IMAGE; REMOTE SENSING; SPATIAL RESOLUTION; URBAN AREA; URBAN PLANNING; WORLDVIEW;

CHINA; GUANGDONG; HONG KONG; SHENZHEN;

EID: 85047250998     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2018.04.050     Document Type: Article

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