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Article

A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing

by 1,† and 1,2,*,†
1
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China
*
Author to whom correspondence should be addressed.
These two authors contribute equally to this work.
Received: 26 January 2019 / Revised: 15 February 2019 / Accepted: 23 February 2019 / Published: 1 March 2019
(This article belongs to the Special Issue Multispectral Image Acquisition, Processing and Analysis)
Due to the complex background and low spatial resolution of the hyperspectral sensor, observed ground reflectance is often mixed at the pixel level. Hyperspectral unmixing (HU) is a hot-issue in the remote sensing area because it can decompose the observed mixed pixel reflectance. Traditional sparse hyperspectral unmixing often leads to an ill-posed inverse problem, which can be circumvented by spatial regularization approaches. However, their adoption has come at the expense of a massive increase in computational cost. In this paper, a novel multiscale hierarchical model for a method of sparse hyperspectral unmixing is proposed. The paper decomposes HU into two domain problems, one is in an approximation scale representation based on resampling the method’s domain, and the other is in the original domain. The use of multiscale spatial resampling methods for HU leads to an effective strategy that deals with spectral variability and computational cost. Furthermore, the hierarchical strategy with abundant sparsity representation in each layer aims to obtain the global optimal solution. Both simulations and real hyperspectral data experiments show that the proposed method outperforms previous methods in endmember extraction and abundance fraction estimation, and promotes piecewise homogeneity in the estimated abundance without compromising sharp discontinuities among neighboring pixels. Additionally, compared with total variation regularization, the proposed method reduces the computational time effectively. View Full-Text
Keywords: hyperspectral unmixing; hierarchical sparsity constraint; multiscale; spatial regularization hyperspectral unmixing; hierarchical sparsity constraint; multiscale; spatial regularization
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MDPI and ACS Style

Zou, J.; Lan, J. A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing. Remote Sens. 2019, 11, 500. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11050500

AMA Style

Zou J, Lan J. A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing. Remote Sensing. 2019; 11(5):500. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11050500

Chicago/Turabian Style

Zou, Jinlin, and Jinhui Lan. 2019. "A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing" Remote Sensing 11, no. 5: 500. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11050500

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