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ESA-EUSC-JRC 2011 Image Information Mining: Geospatial Intelligence from Earth Observation Conference, Ipsra : Italie (2011)
A Novel Data Compression Techniques for Remote Sensing Data Mining
Avid Roman Gonzalez 1, Miguel Veganzones 2, Manuel Grana 3, Mihai Datcu 1, 4
(30/03/2011)

In this article we propose a parameter-free method for Remote Sensing (RS) image databases Data Mining (DM). DM of RS images requires methodologies robust to the diversity of context found in such large datasets, as well as methodologies with low computational costs and low memory requirements. The methodology that we propose is based on the Normalized Compression Distance (NCD) over lossless compressed data. Normalized Compression Distance is a measure of similarity between two data files using the compression factor as an approximation to the Kolmogorov complexity. This approach allows to directly compare information from two images using the lossless compressed original files, and avoiding the feature extraction/selection process commonly used in pattern recognition techniques. This shortcut makes the proposed methodology suitable for DM applications in RS. We provided a classification experiment with hyperspectral data exemplarizing our methodology and comparing it with common methodologies found on the literature.
1 :  Laboratoire Traitement et Communication de l'Information [Paris] (LTCI)
Télécom ParisTech – CNRS : UMR5141
2 :  UPV/EHU
Grupo de Inteligencia Computacional
3 :  Distributed Systems Group [UPV/EHU]
University of the Basque Country, UPV/EHU
4 :  German Aerospace Center (DLR)
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Sciences de l'ingénieur/Traitement du signal et de l'image

Informatique/Traitement du signal et de l'image
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