Sylvia Schmitz
I am a scientific researcher and PhD candidate at Fraunhofer IOSB and the Institute of Photogrammetry and Remote Sensing at KIT. My research focuses on the developement of learning based algorithms for the analysis and interpretation of polarimetric SAR image data in remote sensing applications. In this context, I am particularly interested in topics such as the combination of model-based and data-driven approaches as well as multimodal data fusion and knowledge representation.
The openness of the Python community and their willingness to share their knowledge and provide freely available tools excites me and assists me in making very fast progress in my scientific work.
Talks
Explorative Analysis and Visualization of High-dimensional Remote Sensing Data Using UMAP
How can the information content of large and complex remote sensing data sets be easily grasped and evaluated? And in which way is it possible to identify the potential of such data sets with respect to concrete objectives? Methods from the field of manifold learning, for which implementations are available as ready-to-use Python packages, are a good remedy. This talk focuses on the application of the dimension reduction algorithm Uniform Manifold Approximation and Projection (UMAP) for the visualization of high-dimensional remote sensing data.