A novel inequality-constrained weighted linear mixture model for endmember variability

2021 ◽  
Vol 257 ◽  
pp. 112359
Author(s):  
Jie Yu ◽  
Bin Wang ◽  
Yi Lin ◽  
Fengting Li ◽  
Jianqing Cai
Keyword(s):  
Mixture Model ◽  
Linear Mixture Model ◽  
Endmember Variability

Analysis of Forest Foliage Spectra Using a Multivariate Mixture Model

1997 ◽  
Vol 5 (3) ◽  
pp. 167-173 ◽  
Author(s):  
Christine A. Hlavka ◽  
David L. Peterson ◽  
Lee F. Johnson ◽  
Barry Ganapol
Keyword(s):  
Mixture Model ◽  
Near Infrared ◽  
Photon Absorption ◽  
Chemical Measurements ◽  
Near Infrared Spectra ◽  
Photon Absorption And Scattering ◽  
Linear Mixture Model ◽  
Wet Chemical ◽  
Nitrogen Bonds ◽  
Reflectance Measurements

Wet chemical measurements and near infrared spectra of dry ground leaf samples were analysed to test a multivariate regression technique for estimating component spectra. The technique is based on a linear mixture model for log(1/ R) pseudoabsorbance derived from diffuse reflectance measurements. The resulting unmixed spectra for carbohydrates, lignin and protein resemble the spectra of extracted plant carbohydrates, lignin and protein. The unmixed protein spectrum has prominent absorption peaks at wavelengths that have been associated with nitrogen bonds. It therefore appears feasible to incorporate the linear mixture model in whole leaf models of photon absorption and scattering so that effects of varying nitrogen and carbon concentration on leaf reflectance may be simulated.

scholarly journals The Autoregressive Linear Mixture Model: A Time-Series Model for an Instantaneous Mixture of Network Processes

2020 ◽  
Vol 68 ◽  
pp. 4481-4496
Author(s):  
Addison W. Bohannon ◽  
Vernon J. Lawhern ◽  
Nicholas R. Waytowich ◽  
Radu V. Balan
Keyword(s):  
Time Series ◽  
Mixture Model ◽  
Time Series Model ◽  
Linear Mixture Model

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.

A Novel Blind Spectral Unmixing Method Based on Error Analysis of Linear Mixture Model

2014 ◽  
Vol 11 (7) ◽  
pp. 1180-1184 ◽  
Author(s):  
Chunzhi Li ◽  
Faming Fang ◽  
Aimin Zhou ◽  
Guixu Zhang
Keyword(s):  
Error Analysis ◽  
Mixture Model ◽  
Spectral Unmixing ◽  
Linear Mixture Model
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