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Compression-based self-organizing recognizer PRDC-CSOR with preliminary application to EO-image analysis

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1 Author(s)
Watanabe, T. ; Grad. Sch. of Inf. Syst., Univ. of Electro-Commun., Chofu, Japan

This paper introduces a new data analyzer, the compression-based self-organizing recognizer PRDC-CSOR, with a preliminary application to an EO-image. PRDC-CSOR is an application of the authors' previously proposed pattern representation scheme using data compression (PRDC). Contrary to the traditional statistical model based recognition system methods, PRDC-CSOR constructs itself by using incoming data only. The basic tool, compressibility, is an approximation of the Kolmogorov complexity K(x) defined on an individual text x as a countermeasure to the Shannon entropy H(X) defined on an ensemble X. Due to this feature, a highly automatic self-organizing recognition system becomes possible as shown in this paper.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Date of Conference:

22-27 July 2012