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A Classification Algorithm for Hyperspectral Data Based on Synergetics Theory : Volume I-7, Issue 1 (17/07/2012)

By Cerra, D.

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Book Id: WPLBN0004013728
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: A Classification Algorithm for Hyperspectral Data Based on Synergetics Theory : Volume I-7, Issue 1 (17/07/2012)  
Author: Cerra, D.
Volume: Vol. I-7, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Reinartz, P., Cerra, D., & Mueller, R. (2012). A Classification Algorithm for Hyperspectral Data Based on Synergetics Theory : Volume I-7, Issue 1 (17/07/2012). Retrieved from

Description: German Aerospace Center (DLR), Muenchner Strasse 20, Oberpfaffenhofen, 82234 Wessling, Germany. This paper presents a new classification methodology for hyperspectral data based on synergetics theory, which describes the spontaneous formation of patterns and structures in a system through self-organization. We introduce a representation for hyperspectral data, in which a spectrum can be projected in a space spanned by a set of user-defined prototype vectors, which belong to some classes of interest. Each test vector is attracted by a final state associated to a prototype, and can be thus classified. As typical synergetics-based systems have the drawback of a rigid training step, we modify it to allow the selection of user-defined training areas, used to weight the prototype vectors through attention parameters and to produce a more accurate classification map through majority voting of independent classifications. Results are comparable to state of the art classification methodologies, both general and specific to hyperspectral data and, as each classification is based on a single training sample per class, the proposed technique would be particularly effective in tasks where only a small training dataset is available.



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