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
In this paper, a new algorithm for striping noise
reduction in hyperspectral images is proposed. The new algorithm
exploits the orthogonal subspace approach to estimate the striping
component and to remove it from the image, preserving the useful
signal. The algorithm does not introduce artifacts in the data and
also takes into account the dependence on the signal intensity
of the striping component. The effectiveness of the algorithm in
reducing striping noise is experimentally demonstrated on real
data acquired both by airborne and satellite hyperspectral sensors
A new algorithm for hyperspectral image
segmentation based on the statistical approach is presented. The
algorithm is completely unsupervised and relies only on the
spectral information. The hyperspectral image is statistically
characterized by means of the Gaussian Mixture Model (GMM).
Preliminary results obtained on experimental data are presented
and discussed
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.