Cart (Loading....) | Create Account
Close category search window
 

Automatic Urban Water-Body Detection and Segmentation From Sparse ALSM Data via Spatially Constrained Model-Driven Clustering

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xiaohui Yuan ; Dept. of Comput. Sci. & Eng., Univ. of North Texas, Denton, TX, USA ; Sarma, V.

Identifying hydrological features is important for urban planning and disaster assessment. Data spatial resolution poses challenges in automatic processing. In this letter, we present a novel spatially constrained model-driven clustering method that automatically detects and delineates water bodies in an urban area using airborne laser swath mapping (ALSM) data and imagery. Our method analyzes the modality of the sparseness histogram to decide the existence of water body, followed by clustering. Using the sparseness, clusters are decided by selecting candidate sites. In the iteration of clustering process, new sites are recruited within a close spatial vicinity of the boundary sites. Experiments were conducted using data sets from the city of New Orleans. Our method demonstrated superior robustness regardless of the density of ALSM sample and data discrepancy and very competitive accuracy in comparison with manual tracing, with an overall accuracy above 98%.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:8 ,  Issue: 1 )

Date of Publication:

Jan. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.