Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning

Water (Switzerland)

Volumn 10, Issue 5, 2018, Pages

  Chen, Yang ^a,b   Fan, Rongshuang ^b   Yang, Xiucheng ^c   Wang, Jingxue ^a   Latif, Aamir ^d  

^a LIAONING TECHNICAL UNIVERSITY   (China)

^b CHINESE ACADEMY OF SURVEYING AND MAPPING   (China)

^c UNIVERSITÉ DE STRASBOURG   (France)

^d INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

Author keywords

Convolutional neural networks; Deep learning; High resolution remote sensing images; Superpixel; Urban water bodies

Indexed keywords

CLIMATE CHANGE; CLUSTERING ALGORITHMS; CONVOLUTION; DEEP LEARNING; ECOSYSTEMS; IMAGE SEGMENTATION; ITERATIVE METHODS; NETWORK ARCHITECTURE; NEURAL NETWORKS; PIXELS; REMOTE SENSING; SUPERPIXELS; SURFACE WATERS;

CONVOLUTIONAL NEURAL NETWORK; CONVOLUTIONAL NEURAL NETWORKS (CNN); HIGH RESOLUTION REMOTE SENSING; HIGH RESOLUTION REMOTE SENSING IMAGERY; HIGH RESOLUTION REMOTE SENSING IMAGES; SIMPLE LINEAR ITERATIVE CLUSTERING; SOCIO-ECONOMIC DEVELOPMENT; URBAN WATER BODIES;

EXTRACTION;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; IMAGE RESOLUTION; PIXEL; REMOTE SENSING; SATELLITE IMAGERY; SEGMENTATION; SURFACE WATER; URBAN ECOSYSTEM; URBAN PLANNING; URBAN REGION; WATER RESOURCE;

EID: 85046655775     PISSN: None     EISSN: 20734441     Source Type: Journal    
DOI: 10.3390/w10050585     Document Type: Article

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