Gaussian Mixture Model for Hyperspectral Unmixing with Low-Rank Representation

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.

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Enhanced speaker verification using an adaptive multiple low-rank representation based on the modified adaptive Gaussian mixture model framework

Cluster Computing ◽

10.1007/s10586-017-1051-9 ◽

2017 ◽

Vol 20 (3) ◽

pp. 2333-2347

Author(s):

Tan Dat Trinh ◽

Xinjie Ma ◽

Jin Young Kim ◽

Hyoung Gook Kim

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Speaker Verification ◽

Gaussian Mixture ◽

Low Rank ◽

Model Framework ◽

Low Rank Representation

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Analysis on Characteristics of Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis of Cerebral Aneurysm

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/9751009 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Jun Li ◽

Jin Li ◽

Qin Hu

Keyword(s):

Magnetic Resonance Imaging ◽

Cerebral Aneurysm ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Low Rank ◽

Resonance Imaging ◽

Rank Matrix ◽

Before And After ◽

Low Rank Matrix

This study was to explore the effect of a low-rank matrix denoising (LRMD) algorithm based on the Gaussian mixture model (GMM) on magnetic resonance imaging (MRI) images of patients with cerebral aneurysm and to evaluate the practical value of the LRMD algorithm in the clinical diagnosis of cerebral aneurysm. In this study, the intracranial MRI data of 40 patients with cerebral aneurysm were selected to study the denoising effect of the low-rank matrix denoising algorithm based on the Gaussian mixture model on MRI images of cerebral aneurysm under the influence of Rice noise, to evaluate the PSNR value, SSIM value, and clarity of MRI images before and after denoising. The diagnostic accuracy of MRI images of cerebral aneurysms before and after denoising was compared. The results showed that after the low-rank matrix denoising algorithm based on the Gaussian mixture model, the PSNR, SSIM, and sharpness values of intracranial MRI images of 10 patients were significantly improved ( P < 0.05 ), and the diagnostic accuracy of MRI images of cerebral aneurysm increased from 76.2 ± 5.6 % to 93.1 ± 7.9 % , which could diagnose cerebral aneurysm more accurately and quickly. In conclusion, the MRI images processed based on the low-rank matrix denoising algorithm under the Gaussian mixture model can effectively remove the interference of noise, improve the quality of MRI images, optimize the accuracy of MRI image diagnosis of patients with cerebral aneurysm, and shorten the average diagnosis time, which is worth promoting in the clinical diagnosis of patients with cerebral aneurysm.

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Hyperspectral and Multispectral Image Fusion Based on Low Rank Constrained Gaussian Mixture Model

IEEE Access ◽

10.1109/access.2018.2817071 ◽

2018 ◽

Vol 6 ◽

pp. 16901-16910 ◽

Cited By ~ 4

Author(s):

Baihong Lin ◽

Xiaoming Tao ◽

Yiping Duan ◽

Jianhua Lu

Keyword(s):

Image Fusion ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Multispectral Image ◽

Low Rank

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Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity

Remote Sensing ◽

10.3390/rs11202434 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2434

Author(s):

Qiwen Jin ◽

Yong Ma ◽

Erting Pan ◽

Fan Fan ◽

Jun Huang ◽

...

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Group Structure ◽

Gaussian Mixture ◽

Hyperspectral Data ◽

Conditional Density ◽

Mixed Norm ◽

Group Sparsity ◽

Hyperspectral Unmixing ◽

Endmember Variability

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.

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