| Full text | |
| 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) |