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A Classification Algorithm for Hyperspectral Data based on Synergetics Theory

Cerra, Daniele and Müller, Rupert and Reinartz, Peter (2012) A Classification Algorithm for Hyperspectral Data based on Synergetics Theory. Third Annual Hyperspectral Imaging Conference, 15-16 May 2012, Rome, Italy.

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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 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. We introduce a representation in which each single spectrum, related to an image element in a hyperspectral scene, 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 thus be classified: this establishes a first attempt at performing a pixel-wise 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/78233/
Document Type:Conference or Workshop Item (Speech)
Title:A Classification Algorithm for Hyperspectral Data based on Synergetics Theory
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Cerra, DanieleMF-PBUNSPECIFIED
Müller, RupertMF-PBUNSPECIFIED
Reinartz, PeterMF-PBUNSPECIFIED
Date:2012
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:2
Page Range:pp. 18-23
Status:Published
Keywords:Hyperspectral image analysis, image classification, least squares approximation (LS), synergetics theory.
Event Title:Third Annual Hyperspectral Imaging Conference
Event Location:Rome, Italy
Event Type:international Conference
Event Dates:15-16 May 2012
Organizer:Istituto Nazionale di Geofisica e Vulcanologia
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 Spektrometrische Verfahren und Konzepte der Fernerkundung (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Cerra, Daniele
Deposited On:05 Nov 2012 14:32
Last Modified:08 Feb 2013 09:06

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