%0 Conference Proceedings %T Efficiency analysis for 3D filtering of multichannel images %+ Kharkov National University %+ Institut d'Électronique et des Technologies du numéRique (IETR) %A Kozhemiakin, R.A. %A Rubel, O. %A Abramov, S.K. %A Lukin, V.V. %A Vozel, B. %A Chehdi, K. %< avec comité de lecture %B Image and Signal Processing for Remote Sensing XXII %C Edinburgh, United Kingdom %I SPIE %V 10004 %8 2016-09-26 %D 2016 %R 10.1117/12.2240865 %K Classification (of information) %K Discrete cosine transforms %K Efficiency %K Image analysis %K Remote sensing %K Signal processing %K Different origins %K Discrete Cosine Transform(DCT) %K Efficiency analysis %K Filtering technique %K Multi-polarization %K Multichannel images %K Multichannel remote sensing %K Remote sensing system %K Image processing %Z Computer Science [cs]/Networking and Internet Architecture [cs.NI] %Z Engineering Sciences [physics]/ElectronicsConference papers %X 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. © 2016 SPIE.. %G English %L hal-01484532 %U https://univ-rennes.hal.science/hal-01484532 %~ UNIV-NANTES %~ UNIV-RENNES1 %~ CNRS %~ INSA-RENNES %~ IETR %~ IETR_TEL %~ STATS-UR1 %~ CENTRALESUPELEC %~ UR1-HAL %~ UR1-MATH-STIC %~ UR1-UFR-ISTIC %~ IETR-SHINE %~ IETR-OS %~ TEST-UNIV-RENNES %~ TEST-UR-CSS %~ UNIV-RENNES %~ INSA-GROUPE %~ UR1-MATH-NUM %~ IETR-MULTIP %~ NANTES-UNIVERSITE %~ UNIV-NANTES-AV2022