Expanding the Algorithmic Information Theory Frame for Applications to Earth Observation
"> Figure 1
<p>Visual description and hierarchical clustering of NCD (<b>a</b>), PRDC (<b>b</b>) and Normalized PRDC (<b>c</b>) distances between 60 satellite images of size <math display="inline"> <mrow> <mn>64</mn> <mo>×</mo> <mn>64</mn> </mrow> </math> belonging to 6 classes. Misplacements are circled in red.</p> ">
Abstract
:1. Introduction
2. Preliminaries
3. Expanding the Frame
3.1. Pattern Recognition Based on Data Compression
3.2. Relative Entropy
3.3. Delta Encoding as Conditional Compression
4. Conclusions
Acknowledgements
References
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Cerra, D.; Datcu, M. Expanding the Algorithmic Information Theory Frame for Applications to Earth Observation. Entropy 2013, 15, 407-415. https://doi.org/10.3390/e15010407
Cerra D, Datcu M. Expanding the Algorithmic Information Theory Frame for Applications to Earth Observation. Entropy. 2013; 15(1):407-415. https://doi.org/10.3390/e15010407
Chicago/Turabian StyleCerra, Daniele, and Mihai Datcu. 2013. "Expanding the Algorithmic Information Theory Frame for Applications to Earth Observation" Entropy 15, no. 1: 407-415. https://doi.org/10.3390/e15010407