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 ...

Mass-balance measurements of Icelandic glaciers are sparse through the 20th century. However, the large archive of stereo images available allows estimates of glacier-wide mass balance in decadal time steps since 1945. Combined with climate records, ...

To date, most mass balance studies in Iceland have concentrated on the three largest ice caps. This study turns the focus toward smaller Icelandic glaciers, presenting geodetic mass-balance estimates for 14 of them (total area 1,005 km2 in 2017) from ...

Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new ...

Component trees are region-based representations that encode the inclusion relationship of the threshold sets of an image. These representations are one of the most promising strategies for the analysis and the interpretation of spatial information ...

The European Cooperation in Science and Technology (COST) Action ES1404 HarmoSnow, entitled, A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (2014-2018) aims ...

We propose an unmixing framework for enhancing endmember fraction maps using a combination of spectral and visible images. The new method, data fusion through spatial information-aided learning (DFuSIAL), is based on a learning process for the fusion ...