Chapter 1: Visualization and Data Analysis

Title: Multispectral image noise identification

Author(s): Benoît Vozel, Kacem Chehdi, Nadget Benazzouz

Address: IETR-TSI2M, University of Rennes 1 – ENSSAT, UMR CNRS 6164 BP 80518, 22305 Lannion Cedex, France | IETR-TSI2M, University of Rennes 1 – ENSSAT, UMR CNRS 6164 BP 80518, 22305 Lannion Cedex, France | IETR-TSI2M, University of Rennes 1 – ENSSAT, UMR CNRS 6164 BP 80518, 22305 Lannion Cedex, France

Reference: Atlantic Europe Conference on Remote Imaging and Spectroscopy pp. 15 - 20

Abstract/Summary: Nowadays an obvious tendency in imaging and remote sensing is the use of multichannel (multi- and hyper-spectral) systems. Obtained images are practically never noise-free. Inherent noise and other distortions deteriorate image quality resulting in a common necessity to suppress noise and remove distortions prior to further stages of image processing like classification, interpreting. Note that noise and distortion type as well as their basic parameters can be different for different channels. Furthermore, they can vary in time. Taking into account a rather large size of images offered by modern imaging systems (PIMHAI CASI images), it becomes clear that it is necessary to minimise interactive operations at early stages of image processing and analysis. This paper deals with the problem of identifying the nature of the noise and estimating its statistical parameters from the observed multi-channel image in order to be able to apply the most appropriate processing algorithm and to optimise analysis afterwards. We focus our attention on two main classes of degraded images, the first one being degraded by an additive noise, the second one by a multiplicative noise. To improve the identification rate, we propose an unsupervised classification through a multi-thresholding method in order to localise homogeneous regions. For the accuracy of the estimation of the noise statistical parameters, we distinguish the corresponding local estimates statistical series according to the number of pixels taken into account to calculate them. Some experimental studies on 2005 PIMHAI CASI data show the efficiency and the robustness of the whole method.

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