Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning

Remote Sensing of Environment

Volumn 216, Issue , 2018, Pages 139-153

  Kellenberger, Benjamin ^a   Marcos, Diego ^a   Tuia, Devis ^a  

^a WAGENINGEN UNIVERSITY   (Netherlands)

Author keywords

Animal census; Convolutional Neural Networks; Deep learning; Object detection; Unmanned Aerial Vehicles; Wildlife monitoring

Indexed keywords

AGRICULTURE; AIRCRAFT DETECTION; ANTENNAS; CONSERVATION; CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DEEP NEURAL NETWORKS; LARGE DATASET; LEARNING ALGORITHMS; MAMMALS; OBJECT RECOGNITION; SURVEYS; UNMANNED AERIAL VEHICLES (UAV);

AERIAL VEHICLE; ANIMAL CENSUS; BEST PRACTICES; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; IMBALANCED DATASET; OBJECTS DETECTION; UNMANNED AERIAL VEHICLE; VEHICLE IMAGES; WILDLIFE MONITORING;

OBJECT DETECTION;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CENSUS; DATA ACQUISITION; DATA SET; DETECTION METHOD; ENDANGERED SPECIES; GENETIC ALGORITHM; INSTRUMENTATION; LIVESTOCK; MAMMAL; SATELLITE IMAGERY; WILD POPULATION;

ANIMALIA; MAMMALIA;

EID: 85049529086     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2018.06.028     Document Type: Article

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