Exploiting ConvNet Diversity for Flooding Identification
Published in IEEE Geoscience Remote Sensing Letters, 2018
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.
Recommended citation: Keiller Nogueira, Samuel G. Fadel, Ícaro C. Dourado, Rafael de Oliveira Werneck, Javier A. V. Muñoz, Otávio A. B. Penatti, Rodrigo Tripodi Calumby, Lin Tzy Li, Jefersson A. dos Santos, and Ricardo da Silva Torres. Exploiting convnet diversity for flooding identification. IEEE Geosci. Remote Sensing Lett., 15(9):1446–1450, 2018.
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