ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume II-8
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-8, 111–116, 2014
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-8, 111–116, 2014

  27 Nov 2014

27 Nov 2014

SPCA Assisted Correlation Clustering of Hyperspectral Imagery

A. Mehta and O. Dikshit A. Mehta and O. Dikshit
  • Department of Civil Engineering Indian Institute of Technology Kanpur, Kanpur – 208016, Uttar Pradesh, India

Keywords: Hyperspectral Imagery, Correlation Clustering, ORCLUS, PCA, Segmented PCA

Abstract. In this study, correlation clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of correlation clustering lies in its ability to simultaneously perform feature reduction and clustering. This algorithm also allows selection of different sets of features for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. ORCLUS, a correlation clustering algorithm, is implemented and enhanced by making use of segmented principal component analysis (SPCA) instead of principal component analysis (PCA). Further, original implementation of ORCLUS makes use of eigenvectors corresponding to smallest eigenvalues whereas in this study eigenvectors corresponding to maximum eigenvalues are used, as traditionally done when PCA is used as feature reduction tool. Experiments are conducted on three real hyperspectral images. Preliminary analysis of algorithms on real hyperspectral imagery shows ORCLUS is able to produce acceptable results.