The volume of data and information that is available in digital format is increasing at a breathtaking speed. This is attributable, above all, to the following technical and social trends: the digitization of media and processes, the ubiquitous availability of intelligent devices and sensors, the advancement of autonomous agents acting instead of human beings, and the democratization of information production through the World Wide Web where data users become data producers. This flood of data creates competitive advantage for all those who are able to analyze and use this data in a purposeful manner to improve quality and efficiency or to generate entirely new products and services.
With its workforce of ca. 230 employees, the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS combines the competencies and scientific qualities of all engineering disciplines – especially informatics, as well as mathematics, natural sciences, business economics, geo and social sciences – with profound industry expertise.
Fraunhofer IAIS is a global leader in the areas Large Scale Data Mining and Pattern Classification and has gathered years of experience in the evaluation of image and, in particular, hyperspectral data. Processes for the realtime registration of video data, the efficient classification of hyperspectral signatures in agricultural applications and from X-ray fluorescence (XRF) scans as well as for change detection in synthetic aperture radar images have already been developed at IAIS. These processes are being further developed within the framework of the WISA project (Economy-Oriented Strategic Alliance) and continuously adjusted in line with the requirements of the scenario at hand. The expertise in handling large data volumes shown or acquired in publications and funding projects is a great advantage within the framework of the WISA projects, above all for the evaluation of the algorithms that will be developed as large volumes of image data will have to be collected to guarantee the statistical relevance of the examination.