no code implementations • 20 Mar 2018 • Katharina Franz, Ribana Roscher, Andres Milioto, Susanne Wenzel, Jürgen Kusche
We show the detection and tracking results on sea level anomalies (SLA) data from the area of Australia and the East Australia current, and compare our two eddy detection and tracking approaches to identify the most robust and objective method.
no code implementations • 8 Feb 2018 • Lukas Drees, Ribana Roscher, Susanne Wenzel
In our experiments, the estimation of the automatically derived elementary spectra is compared to the estimation obtained by a manually designed spectral library by means of reconstruction error, mean absolute error of the fraction estimates, sum of fractions, $R^2$, and the number of used elementary spectra.
no code implementations • 19 Oct 2017 • Anne Braakmann-Folgmann, Ribana Roscher, Susanne Wenzel, Bernd Uebbing, Jürgen Kusche
We develop a combination of a convolutional neural network (CNN) and a recurrent neural network (RNN) to ana-lyse both the spatial and the temporal evolution of sea level and to suggest an independent, accurate method to predict interannual sea level anomalies (SLA).
no code implementations • 19 Oct 2017 • Anika Bettge, Ribana Roscher, Susanne Wenzel
This paper addresses the land cover classification task for remote sensing images by deep self-taught learning.