NDLI: Sparsity measure based library aided unmixing of hyperspectral image

Content Provider	IET Digital Library
Author	Das, Samiran Routray, Aurobinda Deb, Alok Kanti
Abstract	Availability of a large number of application-specific spectral libraries has generated a great deal of interest in semi-blind unmixing of the hyperspectral image in both remote sensing and signal processing community. This study presents a novel, semi-supervised, parameter-free algorithm which employs sparsity measures for library pruning. The overall algorithm includes sparsity criteria based library pruning and sparse inversion method for abundance computation. In the pruning process, each library element is removed from the spectral library and the corresponding sparse abundance matrix is computed. The library elements which lead to higher sparsity are adjudged as image endmembers, based on the assumption that elimination of actual image endmember enhances sparsity level. The authors also present a detailed exploration of standard sparsity measures. They calculate the abundance of the pruned library by maximising Gini index or pq-norm sparsity, which satisfies the desirable sparsity properties and is easier to compute. The abundance calculation task is solved using the adaptive direction method of multipliers. The experimental results on several real and synthetic image datasets demonstrate the computational efficiency and proficiency the authors’ method in the presence of noise and highly coherent spectral library.
Starting Page	2077
Ending Page	2085
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 12, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5426
Journal	IET Image Processing
Publisher Date	2019-04-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Abundance Calculation Task Abundance Computation Application-specific Spectral Libraries Computational Efficiency Computer Vision And Image Processing Technique Desirable Sparsity Property Earth Sciences Feature Extraction Geography And Cartography Computing Geophysical Image Processing Highly Coherent Spectral Library Hyperspectral Image Image Classification Image Endmembers Image Recognition Image Representation Interpolation And Function Approximation Iterative Method Knowledge Engineering Technique Learning in AI Library Aided Unmixing Library Pruning Linear Algebra Norm Sparsity Numerical Analysis Parameter-free Algorithm Proficiency Pruned Library Real Image Datasets Remote Sensing Semiblind Unmixing Signal Processing Community Sparse Abundance Matrix Sparse Inversion Method Sparse Matrix Sparsity Level Sparsity Measure Standard Sparsity Measures Statistics Synthetic Image Datasets
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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