Exploiting ConvNet diversity for flooding identification

Research output: Journal contributionsJournal articlesResearchpeer-review


  • Keiller Nogueira
  • Samuel G. Fadel
  • Icaro C. Dourado
  • Rafael O. De Werneck
  • Javier A.V. Munoz
  • Otavio A.B. Penatti
  • Rodrigo T. Calumby
  • Lin Tzy Li
  • Jefersson A. Dos Santos
  • Ricardo Da S. Torres

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.

Original languageEnglish
Article number8398414
JournalIEEE Geoscience and Remote Sensing Letters
Issue number9
Pages (from-to)1446-1450
Number of pages5
Publication statusPublished - 09.2018

    Research areas

  • Flooding identification, inundation, MediaEval, natural disaster, remote sensing, satellites