Next Article in Journal
In Situ/Remote Sensing Integration to Assess Forest Health—A Review
Previous Article in Journal
Detection of High-Density Crowds in Aerial Images Using Texture Classification
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(6), 467; doi:10.3390/rs8060467

Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data

Interfaculty Department of Geoinformatics—Z_GIS, University of Salzburg, Schillerstr. 30, 5020 Salzburg, Austria
Academic Editors: Soe Myint and Prasad S. Thenkabail
Received: 1 March 2016 / Revised: 13 May 2016 / Accepted: 21 May 2016 / Published: 7 June 2016
View Full-Text   |   Download PDF [27236 KB, uploaded 7 June 2016]   |  

Abstract

The classes in fuzzy classification schemes are defined as fuzzy sets, partitioning the feature space through fuzzy rules, defined by fuzzy membership functions. Applying fuzzy classification schemes in remote sensing allows each pixel or segment to be an incomplete member of more than one class simultaneously, i.e., one that does not fully meet all of the classification criteria for any one of the classes and is member of more than one class simultaneously. This can lead to fuzzy, ambiguous and uncertain class assignation, which is unacceptable for many applications, indicating the need for a reliable defuzzification method. Defuzzification in remote sensing has to date, been performed by “crisp-assigning” each fuzzy-classified pixel or segment to the class for which it best fulfills the fuzzy classification rules, regardless of its classification fuzziness, uncertainty or ambiguity (maximum method). The defuzzification of an uncertain or ambiguous fuzzy classification leads to a more or less reliable crisp classification. In this paper the most common parameters for expressing classification uncertainty, fuzziness and ambiguity are analysed and discussed in terms of their ability to express the reliability of a crisp classification. This is done by means of a typical practical example from Object Based Image Analysis (OBIA). View Full-Text
Keywords: defuzzification; fuzzy classification; completeness; correctness defuzzification; fuzzy classification; completeness; correctness
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

Hofmann, P. Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data. Remote Sens. 2016, 8, 467.

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