The University of Southampton
University of Southampton Institutional Repository

Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches

Zhang, J. and Foody, G.M. (2001) Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches International Journal of Remote Sensing, 22, pp. 615-628. (doi:10.1080/01431160050505883).

Record type: Article


Fully-fuzzy classification approaches have attracted increasing interest recently. These approaches allow for multiple and partial class memberships at the level of individual pixels and accommodate fuzziness in all three stages of a supervised classification of remotely sensed imagery. A fully-fuzzy classification strategy may be deemed more objective and correct than partially-fuzzy approaches where fuzziness is only accommodated in one or two of the three classification stages. This paper describes two approaches to the fully-fuzzy classification of remotely sensed imagery: a statistical approach based on a modified fuzzy c-means clustering algorithm performed in a supervised mode and an artificial neural network based approach. This is followed by the documentation of a case study using Landsat Thematic Mapper (TM) data of an Edinburgh suburb. Both approaches were applied to derive fully-fuzzy classifications of land cover, with fuzzy ground data, critical for training and testing the classifications, derived from indicator kriging. Results confirmed the superiority of fully-fuzzy over their respective partially-fuzzy classification counterparts, which is beneficial given their more relaxed requirements for training pixels (i.e. training pixels need not be pure). Similar accuracies were obtained with the artificial neural network and statistical approaches to classification. It is suggested that due emphasis must be placed on derivation and analysis of fuzzy ground data as well as fuzzy classified data in order to further improve fully-fuzzy classifications.

Full text not available from this repository.

More information

Published date: 2001


Local EPrints ID: 16509
ISSN: 0143-1161
PURE UUID: 7c5ada94-3c21-4a8d-9844-831aff14b952

Catalogue record

Date deposited: 11 Aug 2005
Last modified: 17 Jul 2017 16:43

Export record



Author: J. Zhang
Author: G.M. Foody

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.