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Fletta eftir deild "Rafmagns- og tölvuverkfræðideild (HÍ)"

Fletta eftir deild "Rafmagns- og tölvuverkfræðideild (HÍ)"

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    Blind Hyperspectral Unmixing Using Autoencoders 
    Pálsson, Burkni (University of Iceland, School of Engineering and Natural Sciences, Faculty of Electrical and Computer Engineering, 2023-09)
    The subject of this thesis is blind hyperspectral unmixing using deep learning based autoencoders. Two methods based on autoencoders are proposed and analyzed. Both methods seek to exploit the spatial correlations in the hyperspectral images to improve ...
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    Unsupervised Deep Learning in Remote Sensing with Application to Image Fusion and Denoising 
    Nguyen, Han Van (University of Iceland, School of Engineering and Natural Sciences, Faculty of Electrical and Computer Engineering, 2022-12-08)
    Optical remote sensing (RS) uses optical sensors to create images of the Earth's surface. Those imaging sensors are mounted on spaceborne or airborne vehicles and capture visible, near-infrared, and shortwave infrared radiation reflected from the Earth's ...
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    Deep learning for segmentation of brain MRI - validation on the ventricular system and white matter lesions 
    Atlason, Hans (2021-12)
    Magnetic resonance images (MRIs) enable neuroradiologists to investigate the human brain to look for possible causes of disease. The clinical interpretation of these images is, however, mostly limited to subjective assessment or a rough measurement of ...
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    Hyperspectral Image Denoising Using Low-Rank-Based Methods 
    Zhao, Bin (University of Iceland, School of Engineering and Natural Sciences, Faculty of Electrical and Computer Engineering, 2021-12-16)
    Hyperspectral images (HSIs) acquired by hyperspectral imaging sensors contain hundreds of spectral bands. The abundant spectral information provided by an HSI makes it possible to discriminate different materials in a scene. Therefore, HSIs have ...
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    Image Fusion in Remote Sensing and Quality Evaluation of Fused Images 
    Palsson, Frosti (University of Iceland, School of Engineering and Natural Sciences, Faculty of Electrical and Computer Engineering, 2017-09)
    In remote sensing, acquired optical images of high spectral resolution have usually a lower spatial resolution than images of lower spectral resolution. This is due to physical, cost and complexity constraints. To make the most of the available imagery, ...