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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Exploiting ConvNet Diversity for Flooding Identification

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Author(s):
Nogueira, Keiller [1] ; Fadel, Samuel G. [2] ; Dourado, Icaro C. [2] ; Werneck, Rafael de O. [2] ; Munoz, V, Javier A. ; Penatti, Otavio A. B. [3] ; Calumby, Rodrigo T. [4] ; Li, Lin Tzy [5, 3] ; dos Santos, Jefersson A. [1] ; Torres, Ricardo da S. [5]
Total Authors: 10
Affiliation:
[1] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG - Brazil
[2] Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP - Brazil
[3] Samsung Res & Dev Inst Brazil, BR-13097160 Campinas, SP - Brazil
[4] State Univ Feira de Santana, Dept Exact Sci, BR-44036900 Feira De Santana - Brazil
[5] Munoz, Javier A., V, Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP - Brazil
Total Affiliations: 5
Document type: Journal article
Source: IEEE Geoscience and Remote Sensing Letters; v. 15, n. 9, p. 1446-1450, SEP 2018.
Web of Science Citations: 8
Abstract

Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure toward flood monitoring is based on identifying the area most vulnerable to flooding, which gives authorities relevant regions to focus. In this letter, we propose several methods to perform flooding identification in high-resolution remote sensing images using deep learning. Specifically, some proposed techniques are based upon unique networks, such as dilated and deconvolutional ones, whereas others were conceived to exploit diversity of distinct networks in order to extract the maximum performance of each classifier. The evaluation of the proposed methods was conducted in a high-resolution remote sensing data set. Results show that the proposed algorithms outperformed the state-of-the-art baselines, providing improvements ranging from 1% to 4% in terms of the Jaccard Index. (AU)

FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support type: Research Projects - Thematic Grants
FAPESP's process: 16/18429-1 - A bag-of-graphs approach for cross-modal representations
Grantee:Rafael de Oliveira Werneck
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
Grantee:Nelson Luis Saldanha da Fonseca
Support type: Research Projects - Thematic Grants
FAPESP's process: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
Grantee:Ricardo da Silva Torres
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil
Grantee:Rubens Augusto Camargo Lamparelli
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
Grantee:Leonor Patricia Cerdeira Morellato
Support type: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)