NDLI: Hyperspectral image clustering via sparse dictionary-based anchored regression

Content Provider	IET Digital Library
Author	Huang, Nan Xiao, Liang
Abstract	Clustering for hyperspectral images (HSIs) is a very challenging task because HSIs usually have large spectral variability, high dimensionality, and complex structures. The main issue of this study is to develop an improved sparse subspace clustering (SSC) method for HSIs. As an extension of spectral clustering, SSC algorithm has achieved great success; however, the direct self-representation dictionary which is created by raw samples has poor representation power and also the widely used dictionary learning (DL) such as K-Singular Value Decomposition (K-SVD) faces with the problems of high computational complexity. In this study, the authors propose a novel HSI clustering method based on sparse DL and anchored regression. The proposed method follows three stages: (i) sparse DL; (ii) anchored subspace construction and regression; and (iii) representation-based spectral clustering. Specifically, we adopt a fast sparse DL method under a double sparsity constrained optimising model to capture the intrinsic HSIs. To establish a compact subspace for collaborative representation, we present an anchored subspace construction method by using atoms clustering and grouping methods. Owing to the anchored subspace, we can fast compute the representation coefficients with a predefined projection matrix. Experimental results demonstrate that the proposed method achieves the best performance for the HSIs clustering.
Starting Page	261
Ending Page	269
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5421
Journal	IET Image Processing
Publisher Date	2018-11-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Anchored Subspace Construction Method Collaborative Representation Compact Subspace Complex Structures Computer Vision And Image Processing Technique Data Handling Technique Direct Self-representation Dictionary Fast Sparse DL Method High Computational Complexity High Dimensionality HSIs Clustering Task Hyperspectral Data Sets Hyperspectral Image Image Representation Improved Sparse SubSpace Clustering Method Intrinsic Hyperspectral Signatures Knowledge Engineering Technique Learning in AI Low-dimensional Data Matrix Algebra Nature Image Optical, Image And Video Signal Processing Optimisation Optimisation Technique Pattern Clustering Poor Representation Power Representation-based Spectral Clustering Sparse Dictionary-based Anchored Regression Spectral Variability SSC Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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