Speaker |
Ribana Roscher |
|---|---|
Affiliation |
Freie Universität Berlin |
Date |
Friday, 24 July 2015 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
MPEB 1.02 |
Event series |
Jump Trading/ELLIS CSML Seminar Series |
Abstract |
This talk gives an overview about discriminative and reconstructive classification methods for remote sensing images. In the first part, the most commonly used remote sensing sensors and their value for geoscientific applications are introduced. The second part of the presentation explains the discriminative and reconstructive model component of classifiers. While reconstructive methods are able to provide valuable posterior probabilities and are especially suitable for incremental/sequential learning, discriminative models mostly achieve a higher classification accuracy. This talk will present some advantages that arise when both components are combined and used for classification. Applications mainly focus on landcover classification of multispectral and hyperspectral satellite images. The presentation addresses joint works of the Remote Sensing and Geoinformatics Research Group of FU Berlin and the Institute of Geodesy and Geoinformation of University of Bonn. |
Biography |