Feature selection for tree species identification in very high resolution satellite images

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

    Abstract

    The aim of this study was to provide an effective feature selection for tree species classifiers in mixed-species boreal forest, from a very high resolution optical satellite image. The 35 input features were the 5 input spectral bands (multispectral and panchromatic channels), 9 contextual features derived from the panchromatic channel and 21 segment-wise features computed at three segment sizes around the treetop locations. A variable ranking was first performed to evaluate the relevance of each feature. Then sequential forward selection was carried out using k-nearest neighbors (kNN) and Linear Discriminant Analysis classifiers. The results suggested that a reasonable feature set would contain 6 to 10 features, mostly from input bands and contextual features. On such a feature set, the best kNN classifier (k=5) returned classification accuracies of 76% for pine and spruce and 88% for decidous trees, with RMS errors between 1.4% and 3.5% and few mixing with the 4 non-tree classes. (6 refs.)
    Original languageEnglish
    Title of host publicationProceedings
    Subtitle of host publicationIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
    Place of PublicationPiscataway, NJ, USA
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages4461-4464
    ISBN (Electronic)978-1-4577-1005-6
    ISBN (Print)978-1-4577-1003-2
    DOIs
    Publication statusPublished - 2011
    MoE publication typeA4 Article in a conference publication
    EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Vancouver, BC, Canada
    Duration: 24 Jul 201129 Jul 2011

    Conference

    ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
    Abbreviated titleIGARSS 2011
    CountryCanada
    CityVancouver, BC
    Period24/07/1129/07/11

    Fingerprint

    discriminant analysis
    boreal forest
    ranking
    satellite image
    spectral band

    Cite this

    Molinier, M., & Astola, H. (2011). Feature selection for tree species identification in very high resolution satellite images. In Proceedings: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 (pp. 4461-4464). Piscataway, NJ, USA: IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/IGARSS.2011.6132538
    Molinier, Matthieu ; Astola, Heikki. / Feature selection for tree species identification in very high resolution satellite images. Proceedings: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011. Piscataway, NJ, USA : IEEE Institute of Electrical and Electronic Engineers , 2011. pp. 4461-4464
    @inproceedings{9724a4bc7d414b029a26da9964817412,
    title = "Feature selection for tree species identification in very high resolution satellite images",
    abstract = "The aim of this study was to provide an effective feature selection for tree species classifiers in mixed-species boreal forest, from a very high resolution optical satellite image. The 35 input features were the 5 input spectral bands (multispectral and panchromatic channels), 9 contextual features derived from the panchromatic channel and 21 segment-wise features computed at three segment sizes around the treetop locations. A variable ranking was first performed to evaluate the relevance of each feature. Then sequential forward selection was carried out using k-nearest neighbors (kNN) and Linear Discriminant Analysis classifiers. The results suggested that a reasonable feature set would contain 6 to 10 features, mostly from input bands and contextual features. On such a feature set, the best kNN classifier (k=5) returned classification accuracies of 76{\%} for pine and spruce and 88{\%} for decidous trees, with RMS errors between 1.4{\%} and 3.5{\%} and few mixing with the 4 non-tree classes. (6 refs.)",
    author = "Matthieu Molinier and Heikki Astola",
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    isbn = "978-1-4577-1003-2",
    pages = "4461--4464",
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    Molinier, M & Astola, H 2011, Feature selection for tree species identification in very high resolution satellite images. in Proceedings: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011. IEEE Institute of Electrical and Electronic Engineers , Piscataway, NJ, USA, pp. 4461-4464, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, Vancouver, BC, Canada, 24/07/11. https://doi.org/10.1109/IGARSS.2011.6132538

    Feature selection for tree species identification in very high resolution satellite images. / Molinier, Matthieu; Astola, Heikki.

    Proceedings: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011. Piscataway, NJ, USA : IEEE Institute of Electrical and Electronic Engineers , 2011. p. 4461-4464.

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

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    AB - The aim of this study was to provide an effective feature selection for tree species classifiers in mixed-species boreal forest, from a very high resolution optical satellite image. The 35 input features were the 5 input spectral bands (multispectral and panchromatic channels), 9 contextual features derived from the panchromatic channel and 21 segment-wise features computed at three segment sizes around the treetop locations. A variable ranking was first performed to evaluate the relevance of each feature. Then sequential forward selection was carried out using k-nearest neighbors (kNN) and Linear Discriminant Analysis classifiers. The results suggested that a reasonable feature set would contain 6 to 10 features, mostly from input bands and contextual features. On such a feature set, the best kNN classifier (k=5) returned classification accuracies of 76% for pine and spruce and 88% for decidous trees, with RMS errors between 1.4% and 3.5% and few mixing with the 4 non-tree classes. (6 refs.)

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    Molinier M, Astola H. Feature selection for tree species identification in very high resolution satellite images. In Proceedings: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011. Piscataway, NJ, USA: IEEE Institute of Electrical and Electronic Engineers . 2011. p. 4461-4464 https://doi.org/10.1109/IGARSS.2011.6132538