OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion-Reference-Cited by-同舟云学术

OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion

Published:2022 Issue: Volume:15 Page:2341-2356
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Drakonakis Georgios I.¹^ORCID,Tsagkatakis Grigorios²^ORCID,Fotiadou Konstantina¹^ORCID,Tsakalides Panagiotis¹^ORCID

Affiliation:

1. Department of Computer Science, University of Crete, Heraklion, Greece

2. Institute of Computer Science and Foundation of Research and Technology Hellas, University of Crete, Heraklion, Greece

Funder

Marie Skłodowska-Curie

European Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9656571/09723593.pdf?arnumber=9723593

Reference69 articles.

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3. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

4. Fully convolutional networks for semantic segmentation

5. ImageNet Large Scale Visual Recognition Challenge

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