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

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.