A novel measurement matrix optimization approach for hyperspectral unmixing
type: Item
instance of: scholarly article
title: A novel measurement matrix optimization approach for hyperspectral unmixing
zbMATH Open document ID: 1437.94029
DOI: 10.1155/2017/8471024
author: Su Xu
author: Xi-Ping He
published in: Journal of Control Science and Engineering
publication date: 2018-10-15T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 94A08
zbMATH DE Number: 6954288
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/8471024
OpenAlex ID: W2726215905
Wikidata QID: Q59145640
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description: scientific article; zbMATH DE number 6954288