scholarly journals Superpixel-Based Mixed Noise Estimation for Hyperspectral Images Using Multiple Linear Regression

2020 ◽  
Vol 12 (8) ◽  
pp. 1324
Author(s):  
Lei Sun ◽  
Bujin Li ◽  
Yongjian Nian
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares Method ◽  
Real Life ◽  
Principal Component ◽  
Noise Estimation ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Local Sample ◽  
Mixed Noise

HSIs (hyperspectral images) obtained by new-generation hyperspectral sensors contain both electronic noise and photon noise with comparable power. Therefore, both the SI (signal-independent) component and the SD (signal-dependent) component have to be considered. In this paper, a superpixel-based noise estimation algorithm using MLR (multiple linear regression) is proposed for the above mixed noise to estimate the noise standard deviation of both SI component and SD component. First, superpixel segmentation is performed on the first principal component obtained by MNF (minimum noise fraction)-based dimensionality reduction to generate non-overlapping regions with similar pixels. Then, MLR is performed to remove the spectral correlation, and a system of linear equations with respect to noise variances is established according to the local sample statistics calculated within each superpixel. By solving the equations in terms of the least-squares method, the noise variances are determined. The experimental results show that the proposed algorithm provides more accurate local sample statistics, and yields a more accurate noise estimation than the other state-of-the-art algorithms for simulated HSIs. The results of the real-life data also verify the effectiveness of the proposed algorithm.

scholarly journals PENERAPAN REGRESI AKAR LATEN DALAM MENANGANI MULTIKOLINEARITAS PADA MODEL REGRESI LINIER BERGANDA

2014 ◽  
Vol 3 (1) ◽  
pp. 8
Author(s):  
DWI LARAS RIYANTINI ◽  
MADE SUSILAWATI ◽  
KARTIKA SARI
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Least Squares Method ◽  
Simulated Data ◽  
Principal Component ◽  
Latent Root ◽  
Estimation Of Parameters ◽  
Independent Variables ◽  
New Variables

Multicollinearity is a problem that often occurs in multiple linear regression. The existence of multicollinearity in the independent variables resulted in a regression model obtained is far from accurate. Latent root regression is an alternative in dealing with the presence of multicollinearity in multiple linear regression. In the latent root regression, multicollinearity was overcome by reducing the original variables into new variables through principal component analysis techniques. In this regression the estimation of parameters is modified least squares method. In this study, the data used are eleven groups of simulated data with varying number of independent variables. Based on the VIF value and the value of correlation, latent root regression is capable of handling multicollinearity completely. On the other hand, a regression model that was obtained by latent root regression has   value of 0.99, which indicates that the independent variables can explain the diversity of the response variables accurately.

scholarly journals Noise estimation of hyperspectral remote sensing image based on multiple linear regression and wavelet transform

2013 ◽  
Vol 19 (4) ◽  
pp. 639-652 ◽  
Author(s):  
Dong Xu ◽  
Lei Sun ◽  
Jianshu Luo
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Real Data ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Noise Estimation ◽  
Spectral Correlation ◽  
Hyperspectral Remote Sensing Image

Noise estimation of hyperspectral remote sensing image is In this paper, not only the spectral correlation removing is considered, but the spatial correlation removing by wavelet transform is considered as well. Therefore, a new method based on multiple linear regression (MLR) and wavelet transform is proposed to estimate the noise of hyperspectral remote sensing image. Numerical simulation of AVIRIS data is carried out and the real data Hyperion is also used to validate the proposed algorithm. Experimental results show that the method is more adaptive and accurate than the general MLR and the other classified methods.

scholarly journals HYPERSPECTRAL IMAGE DENOISING USING MULTIPLE LINEAR REGRESSION AND BIVARIATE SHRINKAGE WITH 2-D DUAL-TREE COMPLEX WAVELET IN THE SPECTRAL DERIVATIVE DOMAIN

2016 ◽  
Vol 22 (4) ◽  
pp. 822-834
Author(s):  
Lei Sun ◽  
Dong Xu
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Hyperspectral Image ◽  
Random Noise ◽  
Real Life ◽  
Hyperspectral Images ◽  
The Real ◽  
Useful Signal ◽  
Complex Wavelet ◽  
Bivariate Shrinkage

In this paper, a new denoising method is proposed for hyperspectral remote sensing images, and tested on both the simulated and the real-life datacubes. Predicted datacube of the hyperspectral images is calculated by multiple linear regression in the spectral domain based on the strong spectral correlation of the useful signal and the inter-band uncorrelation of the random noise terms in hyperspectral images. A two dimensional dual-tree complex wavelet transform is performed in the spectral derivative domain, where the noise level is elevated temporarily to avoid signal deformation during the wavelet denoising, and then the bivariate shrinkage is used to shrink the wavelet coefficients. Simulated experimental results demonstrate that the proposed method obtains better results than the other denoising methods proposed in the reference, improves the signal to noise ratio up to 0.5dB to 10dB. The real-life data experiment shows that the proposed method is valid and effective.

scholarly journals Mortalité du sapin baumier défolié par la tordeuse des bourgeons de l'épinette dans la région de la Gatineau au Québec

1982 ◽  
Vol 58 (5) ◽  
pp. 213-219 ◽  
Author(s):  
Jean Beaulieu ◽  
Yvan J. Hardy
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Random Number ◽  
Least Squares Method ◽  
Random Number Generator ◽  
Balsam Fir ◽  
Number Generator ◽  
Profit Analysis ◽  
Significant Difference

This paper presents a method of analysis which differentiates between spruce budworm caused mortality and regular mortality on balsam fir in the Gatineau region in Quebec. A first attempt was made using multiple linear regression and a uniform random number generator. In order to overcome the bias inherent to the least squares method when dealing with a binary (0,1) dependent variable, a profit analysis was also conducted. In this case, the parameters and their variance were estimated using likehood method. These two approaches proved to be equivalent when percent budworm caused mortality was compared within the 1958 to 1979 period covered by the data at hand, while the outbreak lasted from 1968 to 1975.In 1979, approximately 55% of the stems had been killed by the budworm, accounting for 53% of the volume. Maple-yellow birch associations were more affected than fir associations although no significant difference was found. Fir mortality was delayed by aerial spraying of insecticides but this advantage disappeared as soon as the spray operations came to an end.

Modeling of TEC over the Iberian Peninsula using PCA decomposition and multiple linear regression on space weather parameters

2021 ◽  
Author(s):  
Anna Morozova ◽  
Tatiana Barlyaeva ◽  
Teresa Barata
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Iberian Peninsula ◽  
Space Weather ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Electron Content ◽  
Time Lags ◽  
Total Electron ◽  
Weather Parameters

<p>The total electron content (TEC) over the Iberian Peninsula was modeled using a three-step procedure. At the 1<sup>st</sup> step the TEC series is decomposed using the principal component analysis (PCA) into several daily modes. Then, the amplitudes of those daily modes is fitted by a multiple linear regression model (MRM) using several types of space weather parameters as regressors. Finally, the TEC series is reconstructed using the PCA daily modes and MRM fitted amplitudes.</p><p>The advantage of such approach is that seasonal variations of the TEC daily modes are automatically extracted by PCA. As space weather parameters we considered proxies for the solar UV and XR fluxes, number of the solar flares, parameters of the solar wind and the interplanetary magnetic field, and geomagnetic indices. Different time lags and combinations of the regressors are tested.</p><p>The possibility to use such TEC models for forecasting was tested. Also, a possibility to use neural networks (NN) instead of MRM is studied.</p>

The Research of Wine Quality Evaluation Based on Multiple Linear Regression

2013 ◽  
Vol 756-759 ◽  
pp. 2489-2493
Author(s):  
Huai Hui Liu ◽  
Wen Long Ji ◽  
Peng Zhang ◽  
Chuan Wen Yao
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Wine Grape ◽  
Wine Quality

Through the establishment of evaluation model based on principal component analysis, select 8 principal components from nearly 30 indexes of wine grape. Then we establish the multiple linear regression model and analyse the association between physicochemical indexes of wine grape and wine, and the influence of physicochemical indexes of wine grape and wine on wine quality. Finally study whether we could use the physicochemical indexes to evaluate the wine quality.

Research on Optimization of "Deoxidation Alloying" of Molten Steel Based on Linear Programming

2020 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Anna Li ◽  
Dongqing Xu
Keyword(s):  
Linear Programming ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Molten Steel ◽  
Programming Model ◽  
Linear Regression Analysis ◽  
Optimal Solution ◽  
Principal Component ◽  
Multiple Linear Regression Analysis ◽  
Main Factors

<p>Aiming at the optimization of the supporting solution for molten steel "deoxidation alloying", the cost of "deoxidation alloying" is minimized from an economic perspective. Using Excel, Eviews and spss software programming, through factor analysis, clustering dimension reduction, principal component analysis Multiple linear regression analysis and linear programming optimization analysis, the author found out the main factors that affected the yield of alloy elements. This paper establishes a multiple linear regression mathematical model that affects the main factors of alloy elements and yield. According to the reference alloy price, the linear programming model is adopted to find the optimal solution of alloy ingredients.</p>

scholarly journals Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

2018 ◽  
Vol 26 (0) ◽  
pp. 170-176 ◽  
Author(s):  
Stephen J.H. Yang ◽  
Owen H.T. Lu ◽  
Anna Y.Q. Huang ◽  
Jeff C.H. Huang ◽  
Hiroaki Ogata ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Academic Performance ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Component ◽  
Component Analysis
Sign in / Sign up

Export Citation Format

Share Document