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Using interchannel correlation in blind evaluation of noise characteristics in multichannel remote sensing images
Author(s): Victoriya V. Abramova; Sergey K. Abramov; Vladimir V. Lukin; Benoit Vozel; Kacem Chehdi
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Paper Abstract

Remote sensing images acquired by modern multichannel sensors are usually corrupted by the mixed noise that contains both signal-independent and signal-dependent components. Information about the characteristics of this noise is usually unknown a-priori and, to be used in further image processing, it is retrieved from images using special blind methods. One of such methods is considered in the paper and its modification taking into account the high level of inter-channel correlation of remote sensing images is proposed. The method is based on line fitting into a set of cluster centers determined on basis of the fourth-order statistical moment analysis in overlapping image blocks. The modification consists in simultaneous evaluation of noise parameters for a group of channels (at least, three) of a multichannel image. To obtain the cluster noise variance estimates for a channel image, it is needed to solve a system of linear equations. The system contains the noise variance estimates evaluated in the difference images obtained for all possible pairs of channel images without repetitions. The effectiveness of the proposed modification is confirmed by numerical simulation results for TID2008 database images and by approbation results on AVIRIS hyperspectral images. It is shown that the obtained noise parameter estimation results are in good agreement with the results provided by the best existing methods whereas the operating speed of the proposed modified method is considerably higher in comparison to the analog.

Paper Details

Date Published: 9 October 2018
PDF: 11 pages
Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 1078909 (9 October 2018); doi: 10.1117/12.2325100
Show Author Affiliations
Victoriya V. Abramova, National Aerospace Univ. (Ukraine)
Sergey K. Abramov, National Aerospace Univ. (Ukraine)
Vladimir V. Lukin, National Aerospace Univ. (Ukraine)
Benoit Vozel, Univ. de Rennes 1, IETR, CNRS (France)
Kacem Chehdi, Univ. de Rennes 1, IETR, CNRS (France)


Published in SPIE Proceedings Vol. 10789:
Image and Signal Processing for Remote Sensing XXIV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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