An improved interval type-2 possibilistic C-means clustering algorithm for interclass maximization

Abstract

Cluster analysis is an important method for data analysis, which is also widely used in remote sensing image classification. An effective clustering algorithm is proposed for the uncertainty caused by the overlap between categories in pattern data and remote sensing images. First, an improved PCM (Possibilistic C-means) algorithm based on distance of cluster center (dc-PCM) is proposed to solve the clustering consistency problem of PCM algorithm. Then, based on dc-PCM algorithm, an improved interval type-2 probabilistic c-means clustering algorithm (IT2-dcPCM) is proposed for interclass maximization. The computational complexity of the proposed algorithm is analyzed, and its performance is evaluated by three data sets, two groups of images segmentation and two groups of remote sensing image classification experiments. The results show that IT2-dcPCM algorithm can assign proper membership degrees to clustering samples, and it has good performance in image segmentation and remote sensing image classification with serious spectral aliasing.