Prof. Dr. - Ing. Morris Riedel

Smart Data Analytics Methods for Remote Sensing Applications

Cavallaro, G., Riedel, M., Benediktsson, J.A., Goetz, M., Runarsson, T., Jonasson, K., Lippert, T.: Smart Data Analytics Methods for Remote Sensing Applications, in conference proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2014), July 13-18, 2014, Quebec City, Canada
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Smart Data Analytics Methods for Remote Sensing Applications

Abstract:
The big data analytics approach emerged that can be interpreted as extracting information from large quantities of scientific data in a systematic way. In order to have a more concrete understanding of this term we refer to its refinement as smart data analytics in order to examine large quantities of scientific data to uncover hidden patterns, unknown correlations, or to extract information in cases where there is no exact formula (e.g. known physical laws). Our concrete big data problem is the classification of classes of land cover types in image-based datasets that have been created using remote sensing technologies, because the resolution can be high (i.e. large volumes) and there are various types such as panchromatic or different used bands like red, green, blue, and nearly infrared (i.e. large variety). We investigate various smart data analytics methods that take advantage of machine learning algorithms (i.e. support vector machines) and state-of-the-art parallelization approaches in order to overcome limitations of big data processing using non-scalable serial approaches.

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