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|Authors: ||Piscini, A.*|
|Title: ||Estimation of Signal to Noise Ratio for Unsupervised Hyperspectral Images|
|Title of journal: ||Quaderni di Geofisica|
|Series/Report no.: ||78/(2010)|
|Publisher: ||Istituto Nazionale di Geofisica e Vulcanologia|
|Issue Date: ||2010|
|Keywords: ||Hypespectral SNR|
|Abstract: ||Hyperspectral sensors have become a standard technology used in the techniques of observation by
satellite and aerial platform for observing the terrestrial ecosystem with particular interest in the
detection and identification of minerals, vegetation, materials and artificial environments. The detection
of real materials depends on the coverage spectral resolution and signal to noise ratio of the spectrometer itself,
as well as the density of the material and the absorption characteristics for the material in the region of
wavelength measured. The signal to noise ratio in particular is one of the parameters that need to be estimated
to establish the quality of images acquired by these systems.
In this contribution a method to estimate the Signal to Noise Ratio (SNR) for unsupervised hyperspectral images
has been investigated.
The method uses the computation of local means and local standard deviations of small homogeneous blocks
in order to define respectively the average signal and the mean noise of the images. If the noise may be
considered mainly addictive the local standard deviation may be considered as the mean noise of image. This
method uses all the spatial information contained in the image scene giving a representative SNR of entire image.
The technique has been engineered in IDL environment and applied to hyperspectral data of HYPER-SIMGA
sensor, developed in the frame of AIRFIRE Project for wildfire detection by airborne remote sensing data.
The SNR results point out that HYPER-SIMGA SWIR images are quite noisy and the spectral range that has to
be taken into account for data analysis is from 1000 to 1700 nm.|
|Appears in Collections:||05.01.01. Data processing|
05.01.04. Statistical analysis
Papers Published / Papers in press
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