NDLI: Band selection of hyperspectral image by sparse manifold clustering

Content Provider	IET Digital Library
Author	Das, Samiran Bhattacharya, Shubhobrata Routray, Aurobinda Deb, Alok Kani
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.
Starting Page	1625
Ending Page	1635
Page Count	11
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 10, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5423
Journal	IET Image Processing
Publisher Date	2019-04-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Band Selection Algorithm Band Selection Framework Band Selection Process Cluster Validity Index Combinatorial Mathematics Computer Vision And Image Processing Technique Feature Extraction Generic Clustering Approach Graph Theory Hyperspectral Image Image Classification Image Data Image Recognition Image Representation Informative Band Optimal Feature Selection Method Optimisation Optimisation Technique Representative Band Sparse Manifold Clustering Unsupervised Manifold Clustering Approach
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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