IMG2DSM: Height Simulation from Single Imagery Using Conditional Generative Adversarial Net

IEEE Geoscience and Remote Sensing Letters

Volumn 15, Issue 5, 2018, Pages 794-798

  Ghamisi, Pedram ^a   Yokoya, Naoto ^b  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

^b Disaster Resilience Science Team   (Japan)

Author keywords

Conditional generative adversarial networks (cGANs); convolutional neural network (CNN); deep learning; digital surface model (DSM); encoder decoder networks; optical images

Indexed keywords

AERIAL PHOTOGRAPHY; CLASSIFICATION (OF INFORMATION); DATA VISUALIZATION; DECODING; DEEP LEARNING; GALLIUM NITRIDE; GAS GENERATORS; GEOMETRICAL OPTICS; III-V SEMICONDUCTORS; NETWORK CODING; NEURAL NETWORKS; OPTICAL SENSORS; PERSONNEL TRAINING; REMOTE SENSING;

ADVERSARIAL NETWORKS; CONVOLUTIONAL NEURAL NETWORKS (CNN); DIGITAL SURFACE MODEL (DSM); ENCODER-DECODER; OPTICAL IMAGE; OPTICAL IMAGING;

ADAPTIVE OPTICS;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; DATA SET; DETECTION METHOD; IMAGE ANALYSIS; NETWORK ANALYSIS; NUMERICAL MODEL; RECONSTRUCTION; SATELLITE DATA; SATELLITE IMAGERY; SPATIAL RESOLUTION;

BADEN-WURTTEMBERG; BRANDENBURG [GERMANY]; GERMANY; POTSDAM; STOCKHOLM [SWEDEN]; SWEDEN; VAIHINGEN AN DER ENZ;

EID: 85042846217     PISSN: 1545598X     EISSN: 15580571     Source Type: Journal    
DOI: 10.1109/LGRS.2018.2806945     Document Type: Article

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