Classification of filtered multichannel images

Dmitriy V. Fevralev; Vladimir V. Lukin; Nikolay N. Ponomarenko; Benoit Vozel; Kacem Chehdi; Andriy Kurekin; Lik-Kwan Shark

doi:10.1117/12.864215

22 October 2010 Classification of filtered multichannel images

Dmitriy V. Fevralev, Vladimir V. Lukin, Nikolay N. Ponomarenko, Benoit Vozel, Kacem Chehdi, Andriy Kurekin, Lik-Kwan Shark

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Proceedings Volume 7830, Image and Signal Processing for Remote Sensing XVI; 78300M (2010) https://doi.org/10.1117/12.864215
Event: SPIE Remote Sensing, 2010, Toulouse, France

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.

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Dmitriy V. Fevralev, Vladimir V. Lukin, Nikolay N. Ponomarenko, Benoit Vozel, Kacem Chehdi, Andriy Kurekin, and Lik-Kwan Shark "Classification of filtered multichannel images", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300M (22 October 2010); https://doi.org/10.1117/12.864215

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