Local Signal-Dependent Noise Variance Estimation From Hyperspectral Textural Images

Uss, Mikhail; Vozel, Benôıt; Lukin, Vladimir; Chehdi, Kacem

doi:10.1109/jstsp.2010.2104312

Cited by 89 publications

(79 citation statements)

References 42 publications

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“…As can be clearly seen from Figure 1, the noise level of the five representative bands is different, and the noise levels of bands 1 and 220 are obviously higher than those of the other three bands. Besides, the previous works correlated with noise estimation of HSI [52][53][54] have demonstrated that the hyperspectral imaging spectrometers adopt very narrow band, which makes the energy acquired in each band not enough to obtain high signal-to-noise ratio (SNR), and the HSI is usually corrupted by wavelength-dependent and sensor-specific noise, which not only degrades the visual quality of the HSI but also limits the precision of the subsequent image interpretation and analysis. That is to say, the noise of HSI is wavelength-dependent, thus the noise levels of different bands are different.…”

Section: The Proposed Su-nlementioning

confidence: 99%

“…To overcome the above mentioned problem, according to the Figure 1 and the previous works correlated with noise estimation of HSI [52][53][54], it is natural to assume that the noise levels at different bands are different. We adopt a simple and efficient noise estimation method based on the multiple regression theory, developed by Bioucas and Nascimento [52], to estimate the noise in each band of HSI.…”

Section: The Proposed Su-nlementioning

confidence: 99%

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Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Mei

et al. 2017

Remote Sensing

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Abstract:Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels in different bands of HSIs, making such methods sensitive to different noise levels. To overcome this problem, the noise levels at different bands are assumed to be different in this paper, and a general sparse unmixing method based on noise level estimation (SU-NLE) under the sparse regression framework is proposed. First, the noise in each band is estimated on the basis of the multiple regression theory in hyperspectral applications, given that neighboring spectral bands are usually highly correlated. Second, the noise weighting matrix can be obtained from the estimated noise. Third, the noise weighting matrix is integrated into the sparse regression unmixing framework, which can alleviate the impact of different noise levels at different bands. Finally, the proposed SU-NLE is solved by the alternative direction method of multipliers. Experiments on synthetic datasets show that the signal-to-reconstruction error of the proposed SU-NLE is considerably higher than those of the corresponding traditional sparse regression unmixing methods without noise level estimation, which demonstrates the efficiency of integrating noise level estimation into the sparse regression unmixing framework. The proposed SU-NLE also shows promising results in real HSIs.Keywords: alternative direction method of multipliers (ADMM); hyperspectral image (HSI); sparse unmixing method based on noise level estimation (SU-NLE)

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Section: The Proposed Su-nlementioning

confidence: 99%

Section: The Proposed Su-nlementioning

confidence: 99%

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Mei

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Due to the high spectral resolution of HS spectrometers, the useful image signal exhibits strong correlations between spectral bands. In contrast, the noise signal is often modelled as a random process that is spatially and spectrally uncorrelated [20][21][22]. The random noise in HS images can be described as:…”

Section: Parametric Noise Modelmentioning

confidence: 99%

“…In this experiment, SD and SI noise are added to the synthetic HS image with SNR = 30 and SDSINR = 1, where the true values of parameters γ sd,p and σ 2 si,p can be obtained according to Equations (21) and (22). The proposed FSSMNE is compared with other three state-of-the-art noise estimation methods, including HS noise parameter estimation (HSNPE) [20], signal-dependent noise estimation (SDNE) [24], and intensity-variance homogeneity classification-based noise estimation (IVHCNE) [25].…”

Section: Experiments On the Synthetic Hs Imagementioning

confidence: 99%

Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

Sun

2017

Remote Sensing

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Accurate approximation of the signal-independent (SI) and signal-dependent (SD) mixed noise from hyperspectral (HS) images is a critical task for many image processing applications where the detection of homogeneous regions plays a key role. Most of the conventional methods empirically divide images into rectangular blocks and then select the homogeneous ones, but it might result in erroneous homogeneity detection, especially for highly textured HS images. To address this challenge, a superpixel segmentation algorithm is proposed in this paper, which can decompose a noisy HS image into patches that adhere to the local structures and hence persist in homogeneous characteristic. A novel spectral similarity measure is defined in the frequency domain to make the superpixel segmentation algorithm more robust to the mixed noise. Combined with an improved scatter-plot-based homogeneous superpixel selection and a multiple linear regression-based noise parameter calculation, our method can accurately estimate SD and SI noise variances from HS images with different noise conditions and various image complexities. We evaluate the proposed method with both synthetic and real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) HS images. Experimental results demonstrate that the proposed noise estimation method outperforms the state-of-the-art methods.

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“…In this paper, only noise estimation algorithms based on signal independent model are analyzed and assessed. There are also some new algorithms with signal dependent model lately [16], [17]. These algorithms are not discussed in this paper.…”

Section: Linear Regression-based Noise Estimation Algrithmsmentioning

confidence: 99%

A Comparative Study on Linear Regression-Based Noise Estimation for Hyperspectral Imagery

Gao

Zhang

et al. 2013

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-In the traditional signal model, signal is assumed to be deterministic, and noise is assumed to be random, additive and uncorrelated to the signal component. A hyperspectral image has high spatial and spectral correlation, and a pixel can be well predicted using its spatial and/or spectral neighbors; any prediction error can be considered from noise. Using this concept, several algorithms have been developed for noise estimation for hyperspectral images. However, these algorithms have not been rigorously analyzed with a unified scheme. In this paper, we conduct a comparative study for such linear regression-based algorithms using simulated images with different signal-to-noise ratio (SNR) and real images with different land cover types. Based on experimental results, instructive guidance is concluded for their practical applications.

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Local Signal-Dependent Noise Variance Estimation From Hyperspectral Textural Images

Cited by 89 publications

References 42 publications

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

A Comparative Study on Linear Regression-Based Noise Estimation for Hyperspectral Imagery

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