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Entropy 2013, 15(1), 407-415; doi:10.3390/e15010407

Expanding the Algorithmic Information Theory Frame for Applications to Earth Observation

*  and
German Aerospace Center (DLR), Remote Sensing Technology Institute, Muenchnerstr. 20, 82234 Wessling, Germany
* Author to whom correspondence should be addressed.
Received: 28 November 2012 / Revised: 20 December 2012 / Accepted: 14 January 2013 / Published: 22 January 2013
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences)
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Recent years have witnessed an increased interest towards compression-based methods and their applications to remote sensing, as these have a data-driven and parameter-free approach and can be thus succesfully employed in several applications, especially in image information mining. This paper expands the algorithmic information theory frame, on which these methods are based. On the one hand, algorithms originally defined in the pattern matching domain are reformulated, allowing a better understanding of the available compression-based tools for remote sensing applications. On the other hand, the use of existing compression algorithms is proposed to store satellite images with added semantic value.
Keywords: algorithmic information theory; data compression; remote sensing algorithmic information theory; data compression; remote sensing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Cerra, D.; Datcu, M. Expanding the Algorithmic Information Theory Frame for Applications to Earth Observation. Entropy 2013, 15, 407-415.

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