Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions

In hyperspectral (HS) imaging, for every pixel a spectrum of wavelengths is captured. These spectra represent material properties, i.e. the spectral signatures. So, classification of HS imagery is based on material properties. This thesis describes a framework to perform pixelwise classification of HS images of a fixed scene subject to varying...

Generating virtual CT from MRI using fully convolutional neural networks with improved structure quality

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