Classification of filtered multichannel images

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 Fevralev, Vladimir Lukin, Nikolay Ponomarenko, Benoit Vozel, Kacem Chehdi, et al.. Classification of filtered multichannel images. Conference on Image and Signal Processing for Remote Sensing XVI, Sep 2010, Toulouse, France. pp.78300M, ⟨10.1117/12.864215⟩. ⟨hal-00947038⟩



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