Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification

Jon A. Benediktsson; Gabriele Cavallaro; Nicola Falco; Ihsen Hedhli; Vladimir A. Krylov; Gabriele Moser; Sebastiano B. Serpico; Josiane Zerubia

doi:10.1007/978-3-319-66330-2_7

Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification

Jon A. Benediktsson^*, Gabriele Cavallaro, Nicola Falco, Ihsen Hedhli, Vladimir A. Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia

^*Corresponding author for this work

University of Iceland

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

8 Citations (Scopus)

Abstract

Current and forthcoming sensor technologies and space missions are providing remote sensing scientists and practitioners with an increasing wealth and variety of data modalities. They encompass multisensor, multiresolution, multiscale, multitemporal, multipolarization, and multifrequency imagery. While they represent remarkable opportunities for the applications, they pose important challenges to the development of mathematical methods aimed at fusing the information conveyed by the input multisource data. In this framework, the present chapter continues the discussion of remote sensing data fusion, which was opened in the previous chapter. Here, the focus is on data fusion for image classification purposes. Both methodological issues of feature extraction and supervised classification are addressed. On both respects, the focus is on hierarchical image models rooted in graph theory. First, multilevel feature extraction is addressed through the latest advances in Mathematical Morphology and attribute profile theory with respect to component trees and trees of shapes. Then, joint supervised classification of multisensor, multiscale, multiresolution, and multitemporal imagery is formulated through hierarchical Markov random fields on quad-trees. Examples of experimental results with data from current VHR optical and SAR missions are shown and analysed.

Original language	English
Title of host publication	Signals and Communication Technology
Publisher	Springer
Pages	277-323
Number of pages	47
DOIs	https://doi.org/10.1007/978-3-319-66330-2_7
Publication status	Published - 2018

Publication series

Name	Signals and Communication Technology
ISSN (Print)	1860-4862
ISSN (Electronic)	1860-4870

Bibliographical note

Funding Information:
This work was partly supported by the French Space Agency (Centre National d’Etudes Spatiales, CNES) through contract no. 8361. The authors would like to thank CNES, the Italian Space Agency (ASI), and GeoEye Inc. and Google Crisis Response for providing the Pléiades, COSMO-SkyMed, and GeoEye imagery used for experiments.

Funding Information:
Acknowledgements This work was partly supported by the French Space Agency (Centre National d’Etudes Spatiales, CNES) through contract no. 8361. The authors would like to thank CNES, the Italian Space Agency (ASI), and GeoEye Inc. and Google Crisis Response for providing the Pléiades, COSMO-SkyMed, and GeoEye imagery used for experiments.

Publisher Copyright:
© Springer International Publishing AG 2018.

Access to Document

10.1007/978-3-319-66330-2_7

Cite this

Benediktsson, J. A., Cavallaro, G., Falco, N., Hedhli, I., Krylov, V. A., Moser, G., Serpico, S. B., & Zerubia, J. (2018). Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification. In Signals and Communication Technology (pp. 277-323). (Signals and Communication Technology). Springer. https://doi.org/10.1007/978-3-319-66330-2_7

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abstract = "Current and forthcoming sensor technologies and space missions are providing remote sensing scientists and practitioners with an increasing wealth and variety of data modalities. They encompass multisensor, multiresolution, multiscale, multitemporal, multipolarization, and multifrequency imagery. While they represent remarkable opportunities for the applications, they pose important challenges to the development of mathematical methods aimed at fusing the information conveyed by the input multisource data. In this framework, the present chapter continues the discussion of remote sensing data fusion, which was opened in the previous chapter. Here, the focus is on data fusion for image classification purposes. Both methodological issues of feature extraction and supervised classification are addressed. On both respects, the focus is on hierarchical image models rooted in graph theory. First, multilevel feature extraction is addressed through the latest advances in Mathematical Morphology and attribute profile theory with respect to component trees and trees of shapes. Then, joint supervised classification of multisensor, multiscale, multiresolution, and multitemporal imagery is formulated through hierarchical Markov random fields on quad-trees. Examples of experimental results with data from current VHR optical and SAR missions are shown and analysed.",

author = "Benediktsson, {Jon A.} and Gabriele Cavallaro and Nicola Falco and Ihsen Hedhli and Krylov, {Vladimir A.} and Gabriele Moser and Serpico, {Sebastiano B.} and Josiane Zerubia",

note = "Funding Information: This work was partly supported by the French Space Agency (Centre National d{\textquoteright}Etudes Spatiales, CNES) through contract no. 8361. The authors would like to thank CNES, the Italian Space Agency (ASI), and GeoEye Inc. and Google Crisis Response for providing the Pl{\'e}iades, COSMO-SkyMed, and GeoEye imagery used for experiments. Funding Information: Acknowledgements This work was partly supported by the French Space Agency (Centre National d{\textquoteright}Etudes Spatiales, CNES) through contract no. 8361. The authors would like to thank CNES, the Italian Space Agency (ASI), and GeoEye Inc. and Google Crisis Response for providing the Pl{\'e}iades, COSMO-SkyMed, and GeoEye imagery used for experiments. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.",

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Benediktsson, JA, Cavallaro, G, Falco, N, Hedhli, I, Krylov, VA, Moser, G, Serpico, SB & Zerubia, J 2018, Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification. in Signals and Communication Technology. Signals and Communication Technology, Springer, pp. 277-323. https://doi.org/10.1007/978-3-319-66330-2_7

Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification. / Benediktsson, Jon A.; Cavallaro, Gabriele; Falco, Nicola et al.
Signals and Communication Technology. Springer, 2018. p. 277-323 (Signals and Communication Technology).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Cavallaro, Gabriele

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AU - Hedhli, Ihsen

AU - Krylov, Vladimir A.

AU - Moser, Gabriele

AU - Serpico, Sebastiano B.

AU - Zerubia, Josiane

N1 - Funding Information: This work was partly supported by the French Space Agency (Centre National d’Etudes Spatiales, CNES) through contract no. 8361. The authors would like to thank CNES, the Italian Space Agency (ASI), and GeoEye Inc. and Google Crisis Response for providing the Pléiades, COSMO-SkyMed, and GeoEye imagery used for experiments. Funding Information: Acknowledgements This work was partly supported by the French Space Agency (Centre National d’Etudes Spatiales, CNES) through contract no. 8361. The authors would like to thank CNES, the Italian Space Agency (ASI), and GeoEye Inc. and Google Crisis Response for providing the Pléiades, COSMO-SkyMed, and GeoEye imagery used for experiments. Publisher Copyright: © Springer International Publishing AG 2018.

PY - 2018

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N2 - Current and forthcoming sensor technologies and space missions are providing remote sensing scientists and practitioners with an increasing wealth and variety of data modalities. They encompass multisensor, multiresolution, multiscale, multitemporal, multipolarization, and multifrequency imagery. While they represent remarkable opportunities for the applications, they pose important challenges to the development of mathematical methods aimed at fusing the information conveyed by the input multisource data. In this framework, the present chapter continues the discussion of remote sensing data fusion, which was opened in the previous chapter. Here, the focus is on data fusion for image classification purposes. Both methodological issues of feature extraction and supervised classification are addressed. On both respects, the focus is on hierarchical image models rooted in graph theory. First, multilevel feature extraction is addressed through the latest advances in Mathematical Morphology and attribute profile theory with respect to component trees and trees of shapes. Then, joint supervised classification of multisensor, multiscale, multiresolution, and multitemporal imagery is formulated through hierarchical Markov random fields on quad-trees. Examples of experimental results with data from current VHR optical and SAR missions are shown and analysed.

AB - Current and forthcoming sensor technologies and space missions are providing remote sensing scientists and practitioners with an increasing wealth and variety of data modalities. They encompass multisensor, multiresolution, multiscale, multitemporal, multipolarization, and multifrequency imagery. While they represent remarkable opportunities for the applications, they pose important challenges to the development of mathematical methods aimed at fusing the information conveyed by the input multisource data. In this framework, the present chapter continues the discussion of remote sensing data fusion, which was opened in the previous chapter. Here, the focus is on data fusion for image classification purposes. Both methodological issues of feature extraction and supervised classification are addressed. On both respects, the focus is on hierarchical image models rooted in graph theory. First, multilevel feature extraction is addressed through the latest advances in Mathematical Morphology and attribute profile theory with respect to component trees and trees of shapes. Then, joint supervised classification of multisensor, multiscale, multiresolution, and multitemporal imagery is formulated through hierarchical Markov random fields on quad-trees. Examples of experimental results with data from current VHR optical and SAR missions are shown and analysed.

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BT - Signals and Communication Technology

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Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification

Abstract

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