Dimensionality reduction of hyperspectral images based on the linear mixture model and dimensionality estimation

2020 ◽  
Author(s):  
Evgeny V. Myasnikov
Keyword(s):  
Dimensionality Reduction ◽  
Mixture Model ◽  
Hyperspectral Images ◽  
Linear Mixture Model

scholarly journals MULTIPLE REFLECTION EFFECTS IN NONLINEAR MIXTURE MODEL FOR HYPERSPECTRAL IMAGE ANALYSIS

2016 ◽  
Vol XLI-B7 ◽  
pp. 295-297
Author(s):  
C. Y. Liu ◽  
H. Ren
Keyword(s):  
Mixture Model ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
High Spectral Resolution ◽  
Bilinear Model ◽  
Hyperspectral Image Analysis ◽  
Linear Mixture Model ◽  
Multiple Materials ◽  
Physical Interactions ◽  
Spectral Channels

Hyperspectral spectrometers can record electromagnetic energy with hundreds or thousands of spectral channels. With such high spectral resolution, the spectral information has better capability for material identification. Because of the spatial resolution, one pixel in hyperspectral images usually covers several meters, and it may contain more than one material. Therefore, the mixture model must be considered. Linear mixture model (LMM) has been widely used for remote sensing target classifications, because of its simplicity and yields reasonable results for smooth surfaces. For rough surfaces, the physical interactions of the light scattered between multiple materials in the scene must be considered. Recently, Generalized Bilinear Model (GBM) is proposed and it includes the double reflection between different materials into a nonlinear model, but it ignores the interactions within the same material. In this study, we propose a modified version of GBM to further consider this effect in our model, called Modified Generalized Bilinear Model (MGBM).

scholarly journals MULTIPLE REFLECTION EFFECTS IN NONLINEAR MIXTURE MODEL FOR HYPERSPECTRAL IMAGE ANALYSIS

2016 ◽  
Vol XLI-B7 ◽  
pp. 295-297
Author(s):  
C. Y. Liu ◽  
H. Ren
Keyword(s):  
Mixture Model ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
High Spectral Resolution ◽  
Bilinear Model ◽  
Hyperspectral Image Analysis ◽  
Linear Mixture Model ◽  
Multiple Materials ◽  
Physical Interactions ◽  
Spectral Channels

Hyperspectral spectrometers can record electromagnetic energy with hundreds or thousands of spectral channels. With such high spectral resolution, the spectral information has better capability for material identification. Because of the spatial resolution, one pixel in hyperspectral images usually covers several meters, and it may contain more than one material. Therefore, the mixture model must be considered. Linear mixture model (LMM) has been widely used for remote sensing target classifications, because of its simplicity and yields reasonable results for smooth surfaces. For rough surfaces, the physical interactions of the light scattered between multiple materials in the scene must be considered. Recently, Generalized Bilinear Model (GBM) is proposed and it includes the double reflection between different materials into a nonlinear model, but it ignores the interactions within the same material. In this study, we propose a modified version of GBM to further consider this effect in our model, called Modified Generalized Bilinear Model (MGBM).

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

Patch Tensor-Based Multigraph Embedding Framework for Dimensionality Reduction of Hyperspectral Images

2020 ◽  
Vol 58 (3) ◽  
pp. 1630-1643 ◽  
Author(s):  
Yang-Jun Deng ◽  
Heng-Chao Li ◽  
Xin Song ◽  
Yong-Jian Sun ◽  
Xiang-Rong Zhang ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Hyperspectral Images
Sign in / Sign up

Export Citation Format

Share Document