Band selection of hyperspectral image by sparse manifold clustering. Issue 10 (4th July 2019)

• Record Type: Journal Article
• Authors: Das, Samiran; Bhattacharya, Shubhobrata; Routray, Aurobinda; Kani Deb, Alok
• Is Part Of: IET image processing. Volume 13:Issue 10(2019)
• Page Start: 1625
• Page End: 1635
• Publication Date: 2019-07-04
• Subjects: feature extraction -- hyperspectral imaging -- image classification -- graph theory -- image representation -- optimisation; band selection algorithm -- hyperspectral image -- sparse manifold clustering -- optimal feature selection method -- informative bands -- band selection process -- image data -- unsupervised manifold clustering approach -- band selection framework -- generic clustering approach -- representative bands -- cluster validity index; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5423
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Band selection of hyperspectral images is an optimal feature selection method, which aims at reducing the computational burden associated with processing the whole data. The significant and informative bands identified by the band selection process lead to efficient, compact representation of the image data and produce a satisfactory performance in the succeeding applications viz. classification, unmixing, target detection and so on. In this study, the authors present an unsupervised manifold clustering approach for band selection, which accounts for different types of scenarios. Unlike other band selection approaches, the authors' proposed manifold clustering framework identifies the informative bands by utilising the interrelation between the bands and accounts for the multi‐manifold structure prevalent in some real images. The proposed band selection framework identifies the optimal number of clusters by cluster validity index, clusters the bands by manifold clustering and select representative bands from each cluster according to graph weight. Their proposed manifold clustering approach is a generic clustering approach, which produces a satisfactory result even when the data contains non‐linearity. The information theoretic performance measures, classification and unmixing performance on real image experiments demonstrate the proficiency of their proposed band selection algorithm.