Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30393
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Exploiting ConvNet Diversity for Flooding Identification
Author(s): Nogueira, Keiller
Fadel, Samuel G
Dourado, Icaro C
Werneck, Rafael de O
Munoz, Javier A V
Penatti, Otavio A B
Calumby, Rodrigo T
Li, Lin Tzy
dos Santos, Jefersson A
Torres, Ricardo da S
Keywords: Geotechnical Engineering and Engineering Geology
Electrical and Electronic Engineering
Issue Date: Sep-2018
Date Deposited: 31-Oct-2019
Citation: Nogueira K, Fadel SG, Dourado IC, Werneck RdO, Munoz JAV, Penatti OAB, Calumby RT, Li LT, dos Santos JA & Torres RdS (2018) Exploiting ConvNet Diversity for Flooding Identification. IEEE Geoscience and Remote Sensing Letters, 15 (9), pp. 1446-1450. https://doi.org/10.1109/lgrs.2018.2845549
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.
DOI Link: 10.1109/lgrs.2018.2845549
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