XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/3: INFORMATION EXTRACTION FROM HYPERSPECTRAL DATA]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: HYPERSPECTRAL DATA CLASSIFICATION USING FACTOR GRAPHS Aliaksei Makarau, Rupert Müller, Gintautas Palubinskas, and Peter Reinartz

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia HYPERSPECTRAL DATA CLASSIFICATION USING FACTOR GRAPHS Aliaksei Makarau, Rupert Müller, Gintautas Palubinskas, and Peter Reinartz German Aerospace Center (DLR) German Remote Sensing Data Center (DFD) bzw. Remote Sensing Technology Institute (IMF) 82234 Oberpfaffenhofen, Germany {aliaksei.makarau, rupert.mueller, gintautas.palubinskas, peter.reinartz}(@dir.de Commission VII/3 KEY WORDS: Hyper spectral, Classification, Training, Reference Data 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 allowed to obtain a competitive accuracy of classification. Employ- ment of training data consisting of 20 randomly selected points for a class allowed to obtain the overall classification accuracy equal to 85.3294 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. graphical model type is not so wide for remotely sensed data in- terpretation. 1 INTRODUCTION Development of new methods for single/multisensory data clas- sification leads to an improvement of the data classification and In this paper a new approach for hyperspectral imagery super- a more precise identification of land-cover classes. Nevertheless, requirements on the methods such as transferability, integration into complex systems, or augmenting ability motivate an employ- ment of probabilistic graphical models [Bishop, 2006]. Applica- tion of probabilistic graphical models becomes more and more popular and efficient solution for image annotation, classification, for definition of semantic link between data and a high level la- id [Lienou et al., 2010], [Bratasanu et al., 2011], [Wang et al., 009]. Factor graphs (FG) were proposed in 1997 [Kschischang et al., 2001] and since then the application of FGs for signal/image processing and recognition is gradually emerging. B. Frey et al. [Frey and Jojic, 2005] performed a work on a comparison of learning and inference methods for probabilistic graphical mod- els (Bayesian networks, Markov random fields, factor graphs). Factor graph is a convenient tool to define complex systems for data processing/interpretation, to expand the systems, allow to model complex interactions among a system parts (e.g. map fea- tures/properties from low to high level), to perform approximate inference on data, or use non full data for plausible decision mak- ing. Nevertheless, application of factor graphs as a more general 137 vised classification using factor graph is proposed. The structure of the factor graph is defined in order to define prior probabilities for input data, to map the input data to a latent variable (a mixture of the input features) and bridge the mixture to a semantic class. A configuration of the factor graph on training data allows to es- timate the parameter set of the graph (probabilistic functions in the factors) and an employment of a fast inference method (Mean field [Frey and Jojic, 2005]) allows to obtain a competitive accu- racy of the hyperspectral data classification. 2 FACTOR GRAPH MODEL FOR CLASSIFICATION Factor graph (undirected probabilistic model) is a more general graphical model than a Bayesian network or a Markov random field. An FG possesses properties of Bayesian network and Markov random field and allows to describe complex relationships among parts of a modeled system. A factor graph is a bipartite graph containing two types of nodes: variable nodes (x;, i — 1..n) and function nodes (factors) (f;(x1,x2,..-,Xn),] = 1..m), where a variable node x; takes value on a finite domain (alphabet A:) [Kschischang et al, 2001]. Figure 1 presents an example of

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