TY  - CONF
AU  - Cavallaro, Gabriele
AU  - Riedel, Morris
AU  - Benediktsson, Jon Atli
AU  - Goetz, Markus
AU  - Runarsson, Tomas
AU  - Jonasson, Kristjan
AU  - Lippert, Thomas
TI  - Smart data analytics methods for remote sensing applications
PB  - IEEE
M1  - FZJ-2014-06179
SP  - 1405 - 1408
PY  - 2014
AB  - 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.
T2  - IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium
CY  - 13 Jul 2014 - 18 Jul 2014, Quebec City (Canada)
Y2  - 13 Jul 2014 - 18 Jul 2014
M2  - Quebec City, Canada
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - <Go to ISI:>//WOS:000349688102037
DO  - DOI:10.1109/IGARSS.2014.6946698
UR  - https://juser.fz-juelich.de/record/172735
ER  -