Change detection from satellite data time series using pixel value distributions

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Abstract

    The paper proposes a novel approach for change detection from image time series. In this approach changes are detected from evaluated distances between the (possibly multivariate) distributions of pixel values. Basing change detection on these distributions facilitates, e.g., joint analysis of images having different resolutions and comparisons of smaller areas against larger images. Furthermore, clouded areas can be excluded from each image separately, allowing the data in the remaining pixels to be utilized independent on the whether the corresponding pixels have been covered by clouds in the other images in the time series. In the paper the proposed method is applied to forest cover change detection using Landsat data covering Mexico.
    Original languageEnglish
    Title of host publicationProceedings of ESA Living Planet Symposium
    PublisherEuropean Space Agency ESA
    ISBN (Print)978-9-2922-1286-5
    Publication statusPublished - 2013
    MoE publication typeNot Eligible
    EventESA Living Planet Symposium 2013 - Edinburgh, United Kingdom
    Duration: 9 Sep 201313 Sep 2013
    Conference number: ESA SP-722

    Conference

    ConferenceESA Living Planet Symposium 2013
    CountryUnited Kingdom
    CityEdinburgh
    Period9/09/1313/09/13

    Fingerprint

    satellite data
    pixel
    time series
    forest cover
    Landsat
    detection
    distribution

    Keywords

    • time series
    • change detection

    Cite this

    Saarela, O., Molinier, M., & Sirro, L. (2013). Change detection from satellite data time series using pixel value distributions. In Proceedings of ESA Living Planet Symposium European Space Agency ESA.
    @inproceedings{5ec1f4960e1b46159e486e3ff9920135,
    title = "Change detection from satellite data time series using pixel value distributions",
    abstract = "The paper proposes a novel approach for change detection from image time series. In this approach changes are detected from evaluated distances between the (possibly multivariate) distributions of pixel values. Basing change detection on these distributions facilitates, e.g., joint analysis of images having different resolutions and comparisons of smaller areas against larger images. Furthermore, clouded areas can be excluded from each image separately, allowing the data in the remaining pixels to be utilized independent on the whether the corresponding pixels have been covered by clouds in the other images in the time series. In the paper the proposed method is applied to forest cover change detection using Landsat data covering Mexico.",
    keywords = "time series, change detection",
    author = "Olli Saarela and Matthieu Molinier and Laura Sirro",
    note = "Project code: 40736",
    year = "2013",
    language = "English",
    isbn = "978-9-2922-1286-5",
    booktitle = "Proceedings of ESA Living Planet Symposium",
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    Saarela, O, Molinier, M & Sirro, L 2013, Change detection from satellite data time series using pixel value distributions. in Proceedings of ESA Living Planet Symposium. European Space Agency ESA, ESA Living Planet Symposium 2013, Edinburgh, United Kingdom, 9/09/13.

    Change detection from satellite data time series using pixel value distributions. / Saarela, Olli; Molinier, Matthieu; Sirro, Laura.

    Proceedings of ESA Living Planet Symposium. European Space Agency ESA, 2013.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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    T1 - Change detection from satellite data time series using pixel value distributions

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    AU - Molinier, Matthieu

    AU - Sirro, Laura

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    N2 - The paper proposes a novel approach for change detection from image time series. In this approach changes are detected from evaluated distances between the (possibly multivariate) distributions of pixel values. Basing change detection on these distributions facilitates, e.g., joint analysis of images having different resolutions and comparisons of smaller areas against larger images. Furthermore, clouded areas can be excluded from each image separately, allowing the data in the remaining pixels to be utilized independent on the whether the corresponding pixels have been covered by clouds in the other images in the time series. In the paper the proposed method is applied to forest cover change detection using Landsat data covering Mexico.

    AB - The paper proposes a novel approach for change detection from image time series. In this approach changes are detected from evaluated distances between the (possibly multivariate) distributions of pixel values. Basing change detection on these distributions facilitates, e.g., joint analysis of images having different resolutions and comparisons of smaller areas against larger images. Furthermore, clouded areas can be excluded from each image separately, allowing the data in the remaining pixels to be utilized independent on the whether the corresponding pixels have been covered by clouds in the other images in the time series. In the paper the proposed method is applied to forest cover change detection using Landsat data covering Mexico.

    KW - time series

    KW - change detection

    M3 - Conference article in proceedings

    SN - 978-9-2922-1286-5

    BT - Proceedings of ESA Living Planet Symposium

    PB - European Space Agency ESA

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    Saarela O, Molinier M, Sirro L. Change detection from satellite data time series using pixel value distributions. In Proceedings of ESA Living Planet Symposium. European Space Agency ESA. 2013