Segmentation-based classification of hyperspectral imagery using projected and correlation clustering techniques. Issue 10 (25th November 2016)

• Record Type: Journal Article
• Authors: Mehta, Anand; Ashapure, Akash; Dikshit, Onkar
• Is Part Of: Geocarto international. Volume 31:Issue 10(2016)
• Page Start: 1045
• Page End: 1057
• Publication Date: 2016-11-25
• Subjects: Segmentation -- projected clustering -- correlation clustering -- classification -- feature reduction -- hyperspectral image; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2015.1110207
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: A partitional clustering-based segmentation is used to carry out supervised classification for hyperspectral images. The main contribution of this study lies in the use of projected and correlation partitional clustering techniques to perform image segmentation. These types of clustering techniques have the capability to concurrently perform clustering and feature/band reduction, and are also able to identify different sets of relevant features for different clusters. Using these clustering techniques segmentation map is obtained, which is combined with the pixel-level support vector machines (SVM) classification result, using majority voting. Experiments are conducted over two hyperspectral images. Combination of pixel-level classification result with the segmentation maps leads to significant improvement of accuracies in both the images. Additionally, it is also observed that, classified maps obtained using SVM combined with projected and correlation clustering techniques results in higher accuracies as compared to classified maps obtained from SVM combined with other partitional clustering techniques.