Next Article in Journal
Distribution and Morphologies of Transverse Aeolian Ridges in ExoMars 2020 Rover Landing Site
Previous Article in Journal
Widespread Decline in Vegetation Photosynthesis in Southeast Asia Due to the Prolonged Drought During the 2015/2016 El Niño
Previous Article in Special Issue
Divide-and-Conquer Dual-Architecture Convolutional Neural Network for Classification of Hyperspectral Images
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2019, 11(8), 911; https://doi.org/10.3390/rs11080911

Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation

1,2, 1, 1,2,*, 1,2, 1,2, 3 and 1,2
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
*
Author to whom correspondence should be addressed.
Received: 12 March 2019 / Revised: 3 April 2019 / Accepted: 11 April 2019 / Published: 15 April 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
  |  
PDF [317 KB, uploaded 15 April 2019]
  |  

Abstract

Gaussian mixture model (GMM) has been one of the most representative models for hyperspectral unmixing while considering endmember variability. However, the GMM unmixing models only have proper smoothness and sparsity prior constraints on the abundances and thus do not take into account the possible local spatial correlation. When the pixels that lie on the boundaries of different materials or the inhomogeneous region, the abundances of the neighboring pixels do not have those prior constraints. Thus, we propose a novel GMM unmixing method based on superpixel segmentation (SS) and low-rank representation (LRR), which is called GMM-SS-LRR. we adopt the SS in the first principal component of HSI to get the homogeneous regions. Moreover, the HSI to be unmixed is partitioned into regions where the statistical property of the abundance coefficients have the underlying low-rank property. Then, to further exploit the spatial data structure, under the Bayesian framework, we use GMM to formulate the unmixing problem, and put the low-rank property into the objective function as a prior knowledge, using generalized expectation maximization to solve the objection function. Experiments on synthetic datasets and real HSIs demonstrated that the proposed GMM-SS-LRR is efficient compared with other current popular methods. View Full-Text
Keywords: hyperspectral image analysis; endmember variability; Gaussian mixture model; superpixel segmentation; low-rank property; Bayesian framework hyperspectral image analysis; endmember variability; Gaussian mixture model; superpixel segmentation; low-rank property; Bayesian framework
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ma, Y.; Jin, Q.; Mei, X.; Dai, X.; Fan, F.; Li, H.; Huang, J. Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation. Remote Sens. 2019, 11, 911.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top