Comparative study on projected clustering methods for hyperspectral imagery classification. Issue 3 (15th March 2016)

• Record Type: Journal Article
• Authors: Mehta, Anand; Dikshit, Onkar
• Is Part Of: Geocarto international. Volume 31:Issue 3(2016)
• Page Start: 296
• Page End: 307
• Publication Date: 2016-03-15
• Subjects: hyperspectral imagery -- projected clustering -- PROCLUS -- DOC -- ORCLUS; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2015.1047416
• Languages: English
• ISSNs: 1010-6049

Abstract

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.