Detecting land-cover modifications from multi-resolution satellite image time series

Francois Petitjean, Jordi Inglada, Pierre Gancarski

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    Abstract

    Frequent high-resolution images will be provided by new satellites such as Venμs, SENTINEL-2 and Landsat Data Continuity Mission. Methods to handle this new type of data are currently developed (see [1] for an example). However, a more frequent observation of the surface of the Earth may be required for some applications. Moreover, the temporal resolution may be reduced by meteorological artifacts. In this work, we propose to take advantage of the higher temporal resolution of satellites with a lower spatial resolution to detect land-cover modification at a high spatial resolution. The proposed approach does not use any fusion step of the high- and low-resolution images. We show that the low spatial resolution satellite image time series (SITS) can be used in order to inform about the stability and relevance of the high spatial resolution classification. Experiments include a wide variety of resolution ratios and study the use of each ratio for the assessment of high resolution classification maps (computed from the high spatial resolution SITS).

    Original languageEnglish
    Title of host publication2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2013) - Proceedings
    Subtitle of host publicationJuly 21–26, 2013, Melbourne, Australia
    Place of PublicationPiscataway, NJ
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages3447-3450
    Number of pages4
    ISBN (Electronic)9781479911141
    DOIs
    Publication statusPublished - 2013
    EventIEEE International Geoscience and Remote Sensing Symposium 2013 - Melbourne Convention and Exhibition Centre, Melbourne, Australia
    Duration: 21 Jul 201326 Jul 2013
    Conference number: 33rd
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6704876 (IEEE Conference Proceedings)

    Conference

    ConferenceIEEE International Geoscience and Remote Sensing Symposium 2013
    Abbreviated titleIGARSS 2013
    CountryAustralia
    CityMelbourne
    Period21/07/1326/07/13
    Internet address

    Keywords

    • Crops
    • Image classification
    • Remote Sensing
    • Time series analysis

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