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Open AccessArticle

Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity

1
Electronic Information School, Wuhan University, Wuhan 430072, China
2
Institute of Aerospace Science and Technology, Wuhan University, Whan 430079, China
3
College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
4
School of Optoelectronic Information Science and Technology, Yantai University, Yantai 264003, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(20), 2434; https://doi.org/10.3390/rs11202434
Received: 26 August 2019 / Revised: 14 October 2019 / Accepted: 15 October 2019 / Published: 20 October 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
In recent years, endmember variability has received much attention in the field of hyperspectral unmixing. To solve the problem caused by the inaccuracy of the endmember signature, the endmembers are usually modeled to assume followed by a statistical distribution. However, those distribution-based methods only use the spectral information alone and do not fully exploit the possible local spatial correlation. When the pixels lie on the inhomogeneous region, the abundances of the neighboring pixels will not share the same prior constraints. Thus, in this paper, to achieve better abundance estimation performance, a method based on the Gaussian mixture model (GMM) and spatial group sparsity constraint is proposed. To fully exploit the group structure, we take the superpixel segmentation (SS) as preprocessing to generate the spatial groups. Then, we use GMM to model the endmember distribution, incorporating the spatial group sparsity as a mixed-norm regularization into the objective function. Finally, under the Bayesian framework, the conditional density function leads to a standard maximum a posteriori (MAP) problem, which can be solved using generalized expectation-maximization (GEM). Experiments on simulated and real hyperspectral data demonstrate that the proposed algorithm has higher unmixing precision compared with other state-of-the-art methods. View Full-Text
Keywords: hyperspectral unmixing; Gaussian mixture model; spatial group sparsity; superpixel segmentation; endmember variability; Bayesian framework hyperspectral unmixing; Gaussian mixture model; spatial group sparsity; superpixel segmentation; endmember variability; Bayesian framework
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MDPI and ACS Style

Jin, Q.; Ma, Y.; Pan, E.; Fan, F.; Huang, J.; Li, H.; Sui, C.; Mei, X. Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity. Remote Sens. 2019, 11, 2434.

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