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A Classification Algorithm for Hyperspectral Images based on Synergetics Theory

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

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

Abstract

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.

Item URL in elib:https://elib.dlr.de/78367/
Document Type:Article
Additional Information:Paper accepted in its final form on the 30th of August 2012.
Title:A Classification Algorithm for Hyperspectral Images based on Synergetics Theory
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Cerra, DanieleMF-PBUNSPECIFIED
Müller, RupertMF-PBUNSPECIFIED
Reinartz, PeterMF-PBUNSPECIFIED
Date:May 2013
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:51
DOI :10.1109/TGRS.2012.2219059
Page Range:pp. 2887-2898
Editors:
EditorsEmail
Plaza, Antonioaplaza@unex.es
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:IEEE Transactions on Geoscience and Remote Sensing
ISSN:0196-2892
ISBN:0196-2892
Status:Published
Keywords:Hyperspectral image analysis, image classification, least squares approximation (LS), synergetics theory.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Cerra, Daniele
Deposited On:13 Nov 2012 12:47
Last Modified:31 Jul 2019 19:38

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