Signal-Dependent Noise Modeling and Model Parameter Estimation in Hyperspectral Images

Abstract

In this paper, a novel method to characterize random noise sources in hyperspectral (HS) images is proposed. Noise is described using a parametric model that accounts for the dependence of noise variance on the useful signal. Such model takes into account the photon noise contribution and is therefore suitable for noise characterization in the data acquired by new-generation HS sensors where electronic noise is not dominant. A new algorithm is developed for the estimation of noise parameters which consists of two steps. First, the noise and signal realizations are extracted fromthe original image by resorting to the multiple-linearregression- based approach. Then, the model parameters are estimated by using a maximum likelihood approach. The new method does not require the intervention of a human operator and the selection of homogeneous regions in the scene. The performance of the new technique is analyzed on simulated HS data. Results on real data are also presented and discussed. Images acquired with a new-generation HS camera are analyzed to give an experimental evidence of the dependence of random noise on the signal level and to show the results of the estimation algorithm. The algorithm is also applied to a well-known Airborne Visible/Infrared Imaging Spectrometer data set in order to show its effectiveness when noise is dominated by the signalindependent term

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

“…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:…”

“…r(x, y, p) 2 (20) where ρ h is the number of pixels in the homogeneous superpixel, and degrees of freedom are reduced from ρ h to ρ h − 4 as four parameters are used in the regression.…”

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

“…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.…”

“…7 with very different land cover types are used in the experiment. Normally, the random noise in AVIRIS images is mainly additive and uncorrelated with the signal [17]. More detailed descriptions are shown in Table II.…”

Gaussian-Cauchy mixture modeling for robust signal-dependent noise estimation

“…The common procedure [1][2][3][4][5][6][7][8] for signal-dependent noise estimation consists in dividing the data or image of interest into uniform or homogeneous regions. Each group of samples is then used for the estimation of a mean-standard deviation pairs.…”