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A Classification Algorithm for Hyperspectral Images based on Synergetics Theory

Cerra, Daniele und Müller, Rupert und Reinartz, Peter (2013) A Classification Algorithm for Hyperspectral Images based on Synergetics Theory. IEEE Transactions on Geoscience and Remote Sensing, 51 (5), Seiten 2887-2898. IEEE Xplore. DOI: 10.1109/TGRS.2012.2219059. ISBN 0196-2892. ISSN 0196-2892.

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Offizielle URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6352889


This paper presents a classification methodology for hyperspectral data based on synergetics theory. Pattern recognition algorithms based on synergetics have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analyzed independently. The spectrum is first projected in a space spanned by a set of user-defined prototype vectors, which belong to some classes of interest, and then attracted by a final state associated to a prototype. The spectrum can thus be classified, establishing a first attempt at performing a pixelwise image classification using notions derived from synergetics. As typical synergetics-based systems have the drawback of a rigid training step, we introduce a new procedure which allows the selection of a training area for each class of interest, used to weight the prototype vectors through attention parameters and to produce a more accurate classification map through plurality vote of independent classifications. As each classification is in principle obtained on the basis of a single training sample per class, the proposed technique could be particularly effective in tasks where only a small training dataset is available. The results presented are promising and often outperform state of the art classification methodologies, both general and specific to hyperspectral data.

Zusätzliche Informationen:Paper accepted in its final form on the 30th of August 2012.
Titel:A Classification Algorithm for Hyperspectral Images based on Synergetics Theory
AutorenInstitution oder E-Mail-Adresse der Autoren
Cerra, DanieleMF-PB
Müller, RupertMF-PB
Reinartz, PeterMF-PB
Datum:Mai 2013
Erschienen in:IEEE Transactions on Geoscience and Remote Sensing
Referierte Publikation:Ja
In Open Access:Nein
In ISI Web of Science:Ja
DOI :10.1109/TGRS.2012.2219059
Seitenbereich:Seiten 2887-2898
HerausgeberInstitution und/oder E-Mail-Adresse der Herausgeber
Plaza, Antonioaplaza@unex.es
Verlag:IEEE Xplore
Name der Reihe:IEEE Transactions on Geoscience and Remote Sensing
Stichwörter:Hyperspectral image analysis, image classification, least squares approximation (LS), synergetics theory.
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben hochauflösende Fernerkundungsverfahren
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Daniele Cerra
Hinterlegt am:13 Nov 2012 12:47
Letzte Änderung:04 Apr 2014 12:05

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