Next Article in Journal
Landslide Displacement Monitoring by a Fully Polarimetric SAR Offset Tracking Method
Previous Article in Journal
Utilizing Multiple Lines of Evidence to Determine Landscape Degradation within Protected Area Landscapes: A Case Study of Chobe National Park, Botswana from 1982 to 2011
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(8), 625; doi:10.3390/rs8080625

Improved Urban Flooding Mapping from Remote Sensing Images Using Generalized Regression Neural Network-Based Super-Resolution Algorithm

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Fenner School of Environment and Society, The Australian National University, Canberra 2601, Australia
3
Commonwealth Scientific and Industrial Research Organization (CSIRO) Land and Water Flagship, Canberra 2601, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 2 June 2016 / Revised: 24 July 2016 / Accepted: 26 July 2016 / Published: 28 July 2016
View Full-Text   |   Download PDF [2595 KB, uploaded 28 July 2016]   |  

Abstract

Urban flooding is a serious natural hazard to many cities all over the world, which has dramatic impacts on the urban environment and human life. Urban flooding mapping has practical significance for the prevention and management of urban flood disasters. Remote sensing images with high temporal resolutions are widely used for urban flooding mapping, but have a limitation of relatively low spatial resolutions. In this study, a new method based on a generalized regression neural network (GRNN) is proposed to achieve improved accuracy in super-resolution mapping of urban flooding (SMUF) from remote sensing images. The GRNN-SMUF algorithm was proposed and then assessed using Landsat 5 and Landsat 8 images of Brisbane city in Australia and Wuhan city in China. Compared to three traditional methods, GRNN-SMUF mapped urban flooding more accurately according to both visual and quantitative assessments. The results of this study will improve the accuracy of urban flooding mapping using easily-available remote sensing images with medium-low spatial resolutions and will be propitious to the prevention and management of urban flood disasters. View Full-Text
Keywords: generalized regression neural network; super-resolution mapping; urban flooding; remote sensing images generalized regression neural network; super-resolution mapping; urban flooding; remote sensing images
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, L.; Xu, T.; Chen, Y. Improved Urban Flooding Mapping from Remote Sensing Images Using Generalized Regression Neural Network-Based Super-Resolution Algorithm. Remote Sens. 2016, 8, 625.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top