A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing
Joint Authors
Source
Journal of Control Science and Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-03
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Electronic engineering
Information Technology and Computer Science
Abstract EN
Each pixel in the hyperspectral unmixing process is modeled as a linear combination of endmembers, which can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance.
However, the limitation of Gaussian random variables on its computational complexity or sparsity affects the efficiency and accuracy.
This paper proposes a novel approach for the optimization of measurement matrix in compressive sensing (CS) theory for hyperspectral unmixing.
Firstly, a new Toeplitz-structured chaotic measurement matrix (TSCMM) is formed by pseudo-random chaotic elements, which can be implemented by a simple hardware; secondly, rank revealing QR factorization with eigenvalue decomposition is presented to speed up the measurement time; finally, orthogonal gradient descent method for measurement matrix optimization is used to achieve optimal incoherence.
Experimental results demonstrate that the proposed approach can lead to better CS reconstruction performance with low extra computational cost in hyperspectral unmixing.
American Psychological Association (APA)
Xu, Su& He, Xiping. 2017. A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing. Journal of Control Science and Engineering،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1173566
Modern Language Association (MLA)
Xu, Su& He, Xiping. A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing. Journal of Control Science and Engineering No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1173566
American Medical Association (AMA)
Xu, Su& He, Xiping. A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing. Journal of Control Science and Engineering. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1173566
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173566