Land Cover Classification Based on Adaptive Interval-Valued Type-2 Fuzzy Clustering Analysis

Abstract

The classic methods, such as FCM, often fail to carry out accurate modeling for the high-level fuzzy uncertainty, and then cause the classification error that should not be ignored in the application. Fortunately, the type-2 fuzzy set is a tool to handle this type of uncertainty. An adaptive interval-valued type-2 fuzzy C-Means clustering algorithm (A-IT2FCM) is proposed, including:(1) a proper modeling method for interval-valued type-2 fuzzy set;(2) an effective type reduction approach by adaptively searching the equivalent type-1 fuzzy sets for the type-2. Three different type-2 fuzzy clustering algorithms are used: the algorithm based on Karnik-Mendel type reduction, a method based on simple type reduction, and A-IT2FCM presented in this article. The experimental data are two data windows of SPOT5 image from Zhuhai and Beijing, China. Results show that, A-IT2FCM outperforms the other algorithms compared. Especially when obvious density difference exists between objects in the data, A-IT2FCM can achieve more accurate class boundaries and higher classification accuracy.