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Hyperspectral data classification using factor graphs

Makarau, Aliaksei and Müller, Rupert and Palubinskas, Gintautas and Reinartz, Peter (2012) Hyperspectral data classification using factor graphs. ISPRS. XXII International Society for Photogrammetry & Remote Sensing Congress (ISPRS 2012), 25 August – 01 September 2012, Melbourne, Australia.

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Abstract

Accurate classification of hyperspectral data is still a competitive task and new classification methods are developed to achieve desired tasks of hyperspectral data use. The objective of this paper is to develop a new method for hyperspectral data classification ensuring the classification model properties like transferability, generalization, probabilistic interpretation, etc. While factor graphs (undirected graphical models) are unfortunately not widely employed in remote sensing tasks, these models possess important properties such as representation of complex systems to model estimation/decision making tasks. In this paper we present a new method for hyperspectral data classification using factor graphs. Factor graph (a bipartite graph consisting of variables and factor vertices) allows factorization of a more complex function leading to definition of variables (employed to store input data), latent variables (allow to bridge abstract class to data), and factors (defining prior probabilities for spectral features and abstract classes; input data mapping to spectral features mixture and further bridging of the mixture to an abstract class). Latent variables play an important role by defining two-level mapping of the input spectral features to a class. Configuration (learning) on training data of the model allows calculating a parameter set for the model to bridge the input data to a class. The classification algorithm is as follows. Spectral bands are separately pre-processed (unsupervised clustering is used) to be defined on a finite domain (alphabet) leading to a representation of the data on multinomial distribution. The represented hyperspectral data is used as input evidence (evidence vector is selected pixelwise) in a configured factor graph and an inference is run resulting in the posterior probability. Variational inference (Mean field) allows to obtain plausible results with a low calculation time. Calculating the posterior probability for each class and comparison of the probabilities leads to classification. Since the factor graphs operate on input data represented on an alphabet (the represented data transferred into multinomial distribution) the number of training samples can be relatively low. Classification assessment on Salinas hyperspectral data benchmark allows to obtain a competitive accuracy of classification. Employment of training data consisting of 20 randomly selected points for a class allows to obtain the overall classification accuracy equal to 85.32% and Kappa equal to 0.8358. Representation of input data on a finite domain discards the curse of dimensionality problem allowing to use large hyperspectral data with a moderately high number of bands.

Item URL in elib:https://elib.dlr.de/77256/
Document Type:Conference or Workshop Item (Speech, Paper)
Title:Hyperspectral data classification using factor graphs
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Makarau, AliakseiIMF-PBUNSPECIFIED
Müller, RupertIMF-PBUNSPECIFIED
Palubinskas, GintautasIMF-PBUNSPECIFIED
Reinartz, PeterIMF-PBUNSPECIFIED
Date:August 2012
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:XXXIX-
Page Range:pp. 137-140
Publisher:ISPRS
Series Name:Technical Commission VII
Status:Published
Keywords:Hyperspectral, Classification, Training, Reference Data
Event Title:XXII International Society for Photogrammetry & Remote Sensing Congress (ISPRS 2012)
Event Location:Melbourne, Australia
Event Type:international Conference
Event Dates:25 August – 01 September 2012
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: Makarau, Aliaksei
Deposited On:11 Sep 2012 10:16
Last Modified:31 Jul 2019 19:37

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