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Change Detection in Multitemporal Hyperspectral Images

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Multitemporal Remote Sensing

Part of the book series: Remote Sensing and Digital Image Processing ((RDIP,volume 20))

Abstract

Multitemporal hyperspectral images provide very detailed spectral information that directly relates to land surface composition. This results in the potential detection of more spectral changes than those visible in the traditional multispectral images. However, the process of extracting changes from hyperspectral images is very complex. This chapter addresses the multiple-change detection problem in multitemporal hyperspectral remote sensing images by analyzing the complexity of this task. An analysis of the concept of “change” is given from the perspective of pixel spectral behaviors, in order to formalize the considered problem. A hierarchical change-detection approach is presented, which aims to identify the possible changes occurred between a pair of hyperspectral images. Changes having discriminable spectral behaviors in hyperspectral images are identified hierarchically by following a top-down structure in an unsupervised way. Experimental results obtained on simulated and real bi-temporal images confirm the validity of the proposed hierarchical change detection approach.

Parts of contents in this chapter are taken from:

S. Liu, L. Bruzzone, F. Bovolo and P. Du, “Hierarchical change detection in multitemporal hyperspectral images,” Geoscience and Remote Sensing, IEEE Transactions on, vol.53, no.1, pp:244–260, 2015.

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Notes

  1. 1.

    Note that the definition of change endmember is conceptually different from the definition of endmembers in spectral unmixing. In the latter case, endmembers are the spectral signatures of pure classes that result combined in mixed pixels due to the limited spatial resolution of the acquisition sensor.

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Acknowledgements

This work was carried out in the framework of the project “Very high spatial and spectral resolution remote sensing: a novel integrated data analysis system”, funded by the Italian Ministry of Education, University and Research (Ministero dell’Istruzione, dell’Università e della Ricerca – MIUR) as a research program of relevant national interest (Programmi di Ricerca di Rilevante Interesse Nazionale – PRIN 2012).

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Authors and Affiliations

  1. Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 5, Povo, 38123, Trento, Italy

    Lorenzo Bruzzone & Sicong Liu

  2. Center for Information and Communication Technology, Fondazione Bruon Kessler, Via Sommarive 18, Povo, 38123, Trento, Italy

    Francesca Bovolo

  3. Department of Geographical Information Science, Nanjing University, Nanjing, China

    Peijun Du

Authors
  1. Lorenzo Bruzzone
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  2. Sicong Liu
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  3. Francesca Bovolo
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  4. Peijun Du
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Corresponding author

Correspondence to Lorenzo Bruzzone .

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Editors and Affiliations

  1. Division of Geoinformatics, KTH Royal Institute of Technology, Stockholm, Sweden

    Yifang Ban

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Bruzzone, L., Liu, S., Bovolo, F., Du, P. (2016). Change Detection in Multitemporal Hyperspectral Images. In: Ban, Y. (eds) Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-47037-5_4

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