A method of remote sensing image auto classification based on interval type-2 fuzzy c-means

Abstract

The pattern set of a remote sensing image contains many kinds of uncertainties. Uncertain information can create imperfect expressions for pattern sets in various pattern recognition algorithms, such as clustering algorithms. Methods based the fuzzy c-means algorithm can manage some uncertainties. As soft clustering methods, They are known to perform better on auto classification of remote sensing images than hard clustering methods. However, if the clusters in a pattern set are of different density and high order uncertainty, performance of FCM may significantly vary depending on the choice of fuzzifiers. Thus, we cannot obtain satisfactory results by using type-1 fuzzy set. Type-2 fuzzy sets permit us to model various uncertainties which cannot be appropriately managed by type-1 fuzzy sets. This paper introduces the theory of interval type-2 fuzzy set into the unsupervised classification of remote sensing images and proposes the automatic remote sensing image classification method based on the interval type-2 fuzzy c-means. Experimental results indicate that our method can obtain more coherent clusters and more accurate boundaries from the data with density difference. Our type-2 fuzzy model can manage the uncertainties of remote sensing images more appropriately and get a more desirable result.