Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

IEEE Geoscience and Remote Sensing Letters

Volumn 14, Issue 5, 2017, Pages 778-782

  Kussul, Nataliia ^a   Lavreniuk, Mykola ^a   Skakun, Sergii ^b   Shelestov, Andrii ^c  

^a INSTITUTE OF HYDROBIOLOGY   (Ukraine)

^b UNIVERSITY OF MARYLAND   (United States)

^c NATIONAL TECHNICAL UNIVERSITY OF UKRAINE   (Ukraine)

Author keywords

Agriculture; convolutional neural networks (CNNs); crop classification; deep learning (DL); joint experiment of crop assessment and monitoring (JECAM); Landsat 8; remote sensing (RS); Sentinel 1; TensorFlow; Ukraine

Indexed keywords

CROPS; DECISION TREES; DEEP LEARNING; IMAGE PROCESSING; NETWORK ARCHITECTURE; OPTICAL DATA PROCESSING; SATELLITE IMAGERY; SUGAR BEETS;

ASSESSMENT AND MONITORING; CROP TYPE CLASSIFICATION; HETEROGENEOUS ENVIRONMENTS; MULTI LAYER PERCEPTRON; MULTI-SOURCE SATELLITE IMAGERY; REMOTE SENSING IMAGES; STATE-OF-THE-ART TECHNIQUES; UNSUPERVISED NEURAL NETWORKS;

REMOTE SENSING;

EID: 85017192157     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2017.2681128     Document Type: Article

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