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Hyperspectral images currently have potential applications in many scientific areas due to their high spectral resolution and consequently their good information contents. Nevertheless, extracting suitable and adequate features from this data is crucial for any analysis and especially for the classification algorithms. To overcome this issue, dimension reduction techniques are proposed and have showed...
Nowadays, hyper-spectral remote sensing imaging systems are able to acquire several hundreds of spectral bands. Increasing spectral bands provide the more information for land cover and separate similarity classes and classification accuracy potentially could increase. Nevertheless classification of hyper-spectral imagery by conventional classifiers suffers from Hughes phenomenon. Namely, by increasing...
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