Add to ORKGNoise estimation of hyperspectral remote sensing image is important for its post-processing and application. 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
In this paper, a novel method to characterize random noise sources in hyperspectral (HS) images is ...
The first step in signal processing is to find an appropriate model for the observed signal. This is...
International audienceA maximum-likelihood method for estimating hyperspectral sensors random noise ...
Noise estimation of hyperspectral remote sensing image is In this paper, not only the spectral corre...
Noise estimation of hyperspectral remote sensing image is important for itspost-processing and appli...
Hyperspectral remote sensing image is easily contaminated by noise, which will affect the applicatio...
Abstract—In the traditional signal model, signal is assumed to be deterministic, and noise is assume...
In this paper, a new denoising method is proposed for hyperspectral remote sensing images, and teste...
Abstract: It is common in hyperspectral remote sensing studies to perform analysis based on derivati...
Many of vegetation studies make use of the vegetation reflectance spectra acquired by hyperspectral ...
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, i...
In this research paper we have analyzed hyperspectral images, hyperspectral imaging technolo- gies a...
The use of hyperspectral sensors has gained relevance in agriculture due to its potential in the phy...
Remote sensor technology has encouraged series of research work in the area of signal and image proc...
Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signa...
In this paper, a novel method to characterize random noise sources in hyperspectral (HS) images is ...
The first step in signal processing is to find an appropriate model for the observed signal. This is...
International audienceA maximum-likelihood method for estimating hyperspectral sensors random noise ...
Noise estimation of hyperspectral remote sensing image is In this paper, not only the spectral corre...
Noise estimation of hyperspectral remote sensing image is important for itspost-processing and appli...
Hyperspectral remote sensing image is easily contaminated by noise, which will affect the applicatio...
Abstract—In the traditional signal model, signal is assumed to be deterministic, and noise is assume...
In this paper, a new denoising method is proposed for hyperspectral remote sensing images, and teste...
Abstract: It is common in hyperspectral remote sensing studies to perform analysis based on derivati...
Many of vegetation studies make use of the vegetation reflectance spectra acquired by hyperspectral ...
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, i...
In this research paper we have analyzed hyperspectral images, hyperspectral imaging technolo- gies a...
The use of hyperspectral sensors has gained relevance in agriculture due to its potential in the phy...
Remote sensor technology has encouraged series of research work in the area of signal and image proc...
Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signa...
In this paper, a novel method to characterize random noise sources in hyperspectral (HS) images is ...
The first step in signal processing is to find an appropriate model for the observed signal. This is...
International audienceA maximum-likelihood method for estimating hyperspectral sensors random noise ...