Expanding the Algorithmic Information Theory Frame for Applications to Earth Observation

Entropy

Daniele Cerra¹ 

关键词: algorithmic information theory;    data compression;    remote sensing;   

DOI  :  10.3390/e15010407

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.