Identification of Algal Blooms in Lakes in the Baltic States Using Sentinel-2 Data and Artificial Neural Networks
Author:
Affiliation:
1. Institute of Geosciences, Vilnius University, Vilnius, Lithuania
2. Institute of Computer Science, Vilnius University, Vilnius, Lithuania
Funder
European Union through the Research Council of Lithuania
Vilnius University Science Promotion Fund for supporting initial stage of research which extended to LMTLT project and this publication
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10436679.pdf?arnumber=10436679
Reference60 articles.
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2. Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters;Brockmann,2016
3. Fast and accurate deep network learning by exponential linear units (ELUs);Clevert
4. Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales
5. The MERIS Case 2 water algorithm
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