Comparative study on projected clustering methods for hyperspectral imagery classification

Abstract

In this study, projected clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of projected clustering lies in its ability to simultaneously perform feature selection and clustering. This framework also allows selection of different sets of dimensions (features/bands) for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. Experiments are conducted on both synthetic and real hyperspectral imagery. For this purpose, projected clustering algorithms are implemented and compared with k-means and k-means preceded by principal component analysis. Preliminary analyses of studied algorithms on synthetic hyperspectral imagery demonstrate good results. For real hyperspectral imagery, only ORCLUS is able to produce acceptable results as compared to other unsupervised methods. The main concern lies with identification of right parameter settings. More experiments are required in this direction.

A Novel Clustering Algorithm Based on DPC and PSO

“…Clustering is the process of grouping a set of data objects into multiple groups or clusters so that objects within a cluster have high similarity but are very dissimilar to objects in other clusters. Clustering as a data mining tool has its roots in many application areas such as biology, security, business intelligence, pattern recognition, Web search [7]- [9], trajectory clustering [10], [11] and astronomy [12]- [14].…”