Statistics – Applications
Scientific paper
Sep 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004esasp.553e..22g&link_type=abstract
Proceedings of ESA-EUSC 2004 - Theory and Applications of Knowledge-Driven Image Information Mining with Focus on Earth Observa
Statistics
Applications
Scientific paper
Important information on the scene changes is contained in temporal image sequences. The generation of the map containing the targets in multitemporal high resolution hyperspectral images is not an easy task. The problematic is the detection of "risk" targets like cars or tracks in situation of multitemporal observations in different illumination conditions and strong background clutter. In the article are introduced unsupervised techniques for target detection. They are based on the extraction of the basic image primitives as spectral signatures or texture parameters, on the analysis of the spectral bands applying different operations between them, as differences, ratios or temporal spectral angular distance, and also on the analysis of the principal components. Other techniques more related with the composition of color of each band as vegetation indexes could help in target detection. Following this approach, illumination invariant indexes are developed in order to detect the strong false alarms. Finally, the different algorithms are combined to optimize the final results. However, images can not only contain the quantitative and objective information obtained by unsupervised algorithms, but also subjective based on knowledge. Knowledge-driven Information Mining System (KIM) is built in order to formalize the knowledge acquisition and the knowledge driven interpretation. It provides solutions how to access to large image data sets through information mining, and content based image retrieval.
Datcu Mihai
Gómez Muñoz I. M.
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