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Proceedings Paper

Efficiency analysis for 3D filtering of multichannel images
Author(s): Ruslan A. Kozhemiakin; Oleksii Rubel; Sergey K. Abramov; Vladimir V. Lukin; Benoit Vozel; Kacem Chehdi
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Paper Abstract

Modern remote sensing systems basically acquire images that are multichannel (dual- or multi-polarization, multi- and hyperspectral) where noise, usually with different characteristics, is present in all components. If noise is intensive, it is desirable to remove (suppress) it before applying methods of image classification, interpreting, and information extraction. This can be done using one of two approaches – by component-wise or by vectorial (3D) filtering. The second approach has shown itself to have higher efficiency if there is essential correlation between multichannel image components as this often happens for multichannel remote sensing data of different origin. Within the class of 3D filtering techniques, there are many possibilities and variations. In this paper, we consider filtering based on discrete cosine transform (DCT) and pay attention to two aspects of processing. First, we study in detail what changes in DCT coefficient statistics take place for 3D denoising compared to component-wise processing. Second, we analyze how selection of component images united into 3D data array influences efficiency of filtering and can the observed tendencies be exploited in processing of images with rather large number of channels.

Paper Details

Date Published: 18 October 2016
PDF: 11 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 1000406 (18 October 2016); doi: 10.1117/12.2240865
Show Author Affiliations
Ruslan A. Kozhemiakin, National Aerospace Univ. (Ukraine)
Oleksii Rubel, National Aerospace Univ. (Ukraine)
Sergey K. Abramov, National Aerospace Univ. (Ukraine)
Vladimir V. Lukin, National Aerospace Univ. (Ukraine)
Benoit Vozel, IETR, CNRS, Univ. de Rennes 1 (France)
Kacem Chehdi, IETR, CNRS, Univ. de Rennes 1 (France)


Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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