Efficiency analysis for 3D filtering of multichannel images

Ruslan A. Kozhemiakin; Oleksii Rubel; Sergey K. Abramov; Vladimir V. Lukin; Benoit Vozel; Kacem Chehdi

doi:10.1117/12.2240865

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18 October 2016 Efficiency analysis for 3D filtering of multichannel images

Ruslan A. Kozhemiakin, Oleksii Rubel, Sergey K. Abramov, Vladimir V. Lukin, Benoit Vozel, Kacem Chehdi

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Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 1000406 (2016) https://doi.org/10.1117/12.2240865
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

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.

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Ruslan A. Kozhemiakin, Oleksii Rubel, Sergey K. Abramov, Vladimir V. Lukin, Benoit Vozel, and Kacem Chehdi "Efficiency analysis for 3D filtering of multichannel images", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 1000406 (18 October 2016); https://doi.org/10.1117/12.2240865

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