Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 15, Issue 9, Pages 1446-1450Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2845549
Keywords
Flooding identification; inundation; MediaEval; natural disaster; remote sensing; satellites
Categories
Funding
- Sao Paulo Research Foundation [2013/50169-1, 2013/50155-0, 2014/50715-9, 2014/12236-1, 2015/24494-8, 2016/18429-1]
- Minas Gerais State Agency for Research and Development [APQ-00449-17]
- National Council for Scientific and Technological Development [312167/2015-6]
- Coordination for the Improvement of Higher Level Personnel [88881.145912/2017-01]
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [14/50715-9] Funding Source: FAPESP
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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.
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