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Synergetics Framework for Hyperspectral Image Classification

Müller, Rupert and Cerra, Daniele and Reinartz, Peter (2013) Synergetics Framework for Hyperspectral Image Classification. In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., pp. 257-262. International Society for Photogrammetry and Remote Sensing. ISPRS Hannover Workshop 2013, 21.-24. Mai 2013, Hannover, Deutschland.

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Official URL: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W1/257/2013/isprsarchives-XL-1-W1-257-2013.html

Abstract

In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics – originally introduced by the physicist H. Haken – is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ecology, chemistry, cosmology, and thermodynamics up to sociology. Although this theory describes general rules for pattern formation it was linked also to pattern recognition. Pattern recognition algorithms based on synergetics theory 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 analysed independently. The classification scheme based on synergetics introduces also methods for spatial regularization to get rid of "salt and pepper" classification results and for iterative parameter tuning to optimize class weights. The paper reports an experiment on a benchmark data set frequently used for method comparisons. This data set consists of a hyperspectral scene acquired by the Airborne Visible Infrared Imaging Spectrometer AVIRIS sensor of the Jet Propulsion Laboratory acquired over the Salinas Valley in CA, USA, with 15 vegetation classes. The results are compared to state-of-the-art methodologies like Support Vector Machines (SVM), Spectral Information Divergence (SID), Neural Networks, Logistic Regression, Factor Graphs or Spectral Angle Mapper (SAM). The outcomes are promising and often outperform state-of-the-art classification methodologies.

Item URL in elib:https://elib.dlr.de/83146/
Document Type:Conference or Workshop Item (Speech)
Title:Synergetics Framework for Hyperspectral Image Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Müller, Rupertrupert.mueller (at) dlr.deUNSPECIFIED
Cerra, Danieledaniele.cerra (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Date:May 2013
Journal or Publication Title:Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 257-262
Editors:
EditorsEmail
Heipke, ChristianIPI
Jacobsen, KarstenIPI
Rottensteiner, FranzIPI
Sörgel, UweIPI
Publisher:International Society for Photogrammetry and Remote Sensing
Series Name:ISPRS Archives
Status:Published
Keywords:Synergetics, hyperspectral image, image classification
Event Title:ISPRS Hannover Workshop 2013
Event Location:Hannover, Deutschland
Event Type:international Conference
Event Dates:21.-24. Mai 2013
Organizer:IPI Hannover
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: Müller, Rupert
Deposited On:28 Jun 2013 11:59
Last Modified:31 Jul 2019 19:41

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