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Proceedings Paper

Classification of filtered multichannel images
Author(s): Dmitriy V. Fevralev; Vladimir V. Lukin; Nikolay N. Ponomarenko; Benoit Vozel; Kacem Chehdi; Andriy Kurekin; Lik-Kwan Shark
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Paper Abstract

A typical tendency in modern remote sensing (RS) is to apply multichannel systems. Images formed by them are in more or less degree noisy. Thus, their pre-filtering can be used for different purposes, in particular, to improve classification. In this paper, we consider methods of multichannel image denoising based on discrete cosine transform (DCT) and analyze how parameters of these methods affect classification. Both component-wise and 3D denoising is studied for three-channel Landsat test image. It is shown that for better determination of different classes, DCT based filters, both component-wise and 3D variants are efficient, but with a different tuning of involved parameters. The parameters can be optimized with respect to either standard MSE or metrics that characterize image visual quality. Best results are obtained with 3D denoising. Although the main conclusions basically coincide for both considered classifiers, Radial Basis Function Neural Network (RBF NN) and Support Vector Machine (SVM), the classification results appear slightly better with RBF NN for the experiment carried out in this paper.

Paper Details

Date Published: 22 October 2010
PDF: 12 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300M (22 October 2010); doi: 10.1117/12.864215
Show Author Affiliations
Dmitriy V. Fevralev, National Aerospace Univ. (Ukraine)
Vladimir V. Lukin, National Aerospace Univ. (Ukraine)
Nikolay N. Ponomarenko, National Aerospace Univ. (Ukraine)
Benoit Vozel, Univ. de Rennes 1 (France)
Kacem Chehdi, Univ. de Rennes 1 (France)
Andriy Kurekin, Plymouth Marine Lab. (United Kingdom)
Lik-Kwan Shark, Univ. of Central Lancashire (United Kingdom)


Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

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