scholarly journals Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation

2019 ◽  
Vol 11 (8) ◽  
pp. 911 ◽  
Author(s):  
Yong Ma ◽  
Qiwen Jin ◽  
Xiaoguang Mei ◽  
Xiaobing Dai ◽  
Fan Fan ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Spatial Data ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Low Rank ◽  
Hyperspectral Unmixing ◽  
Low Rank Representation ◽  
Synthetic Datasets ◽  
Endmember Variability

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.

scholarly journals Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity

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.

scholarly journals Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation

2021 ◽  
Vol 13 (2) ◽  
pp. 223
Author(s):  
Zhenyang Hui ◽  
Shuanggen Jin ◽  
Dajun Li ◽  
Yao Yevenyo Ziggah ◽  
Bo Liu
Keyword(s):  
Transfer Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Principal Component ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Individual Tree ◽  
Terrestrial Lidar ◽  
Initial Segmentation ◽  
Tree Extraction

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

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

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.

Local preserving projections-based feature selection and Gaussian mixture model for machine health assessment

2011 ◽  
Vol 225 (7) ◽  
pp. 1703-1717 ◽  
Author(s):  
J Yu ◽  
M Liu ◽  
H Wu
Keyword(s):  
Feature Selection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Health Assessment ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Health State ◽  
Structure Information ◽  
Machine Health ◽  
Low Dimensional

The sensitivity of various features that are characteristics of machine health may vary significantly under different working conditions. Thus, it is critical to devise a systematic feature selection (FS) approach that provides a useful and automatic guidance on choosing the most effective features for machine health assessment. This article proposes a locality preserving projections (LPP)-based FS approach. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP can find a good linear embedding that preserves local structure information. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. The LPP-based FS approach is based on unsupervised learning technique, which does not need too much prior knowledge to improve its utility in real-world applications. The effectiveness of the proposed approach was evaluated experimentally on bearing test-beds. A novel machine health assessment indication, Gaussian mixture model-based Mahalanobis distance is proposed to provide a comprehensible indication for quantifying machine health state. The proposed approach has shown to provide the better performance with reduced feature inputs than using all original candidate features. The experimental results indicate its potential applications as an effective tool for machine health assessment.

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

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 16901-16910 ◽  
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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