Next Article in Journal
Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands
Previous Article in Journal
An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(9), 885; doi:10.3390/rs9090885

Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines

1
Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede; The Netherlands
2
Geospatial Information Agency (BIG), Jl. Raya Jakarta-Bogor Km. 46, Cibinong, Bogor 16911, Indonesia
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak Mishra and Prasad S. Thenkabail
Received: 19 July 2017 / Revised: 17 August 2017 / Accepted: 21 August 2017 / Published: 25 August 2017
View Full-Text   |   Download PDF [6328 KB, uploaded 28 August 2017]   |  

Abstract

This paper addresses uncertainty modelling of shorelines by comparing fuzzy sets and random sets. Both methods quantify extensional uncertainty of shorelines extracted from remote sensing images. Two datasets were tested: pan-sharpened Pleiades with four bands (Pleiades) and pan-sharpened Pleiades stacked with elevation data as the fifth band (Pleiades + DTM). Both fuzzy sets and random sets model the spatial extent of shoreline including its uncertainty. Fuzzy sets represent shorelines as a margin determined by upper and lower thresholds and their uncertainty as confusion indices. They do not consider randomness. Random sets fit the mixed Gaussian model to the image histogram. It represents shorelines as a transition zone between water and non-water. Their extensional uncertainty is assessed by the covering function. The results show that fuzzy sets and random sets resulted in shorelines that were closely similar. Kappa (κ) values were slightly different and McNemar’s test showed high p-values indicating a similar accuracy. Inclusion of the DTM (digital terrain model) improved the classification results, especially for roofs, inundated houses and inundated land. The shoreline model using Pleiades + DTM performed better than that of using Pleiades only, when using either fuzzy sets or random sets. It achieved κ values above 80%. View Full-Text
Keywords: fuzzy sets; random sets; possibility; probability; shorelines; uncertainty fuzzy sets; random sets; possibility; probability; shorelines; uncertainty
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).

Supplementary material

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

Dewi, R.S.; Bijker, W.; Stein, A. Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines. Remote Sens. 2017, 9, 885.

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