Support Vector Machine (SVM) classifier with small training samples for mapping saltmash wetland at species level

Sikdar M. M. Rasel, Hsing-Chung Chang, Israt Jahan Diti, Tim Glasby

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    Abstract

    Ground truth data collection for species-level mapping is made challenging by limited access and hazardous conditions in some wetland ecosystems. Support Vector Machine (SVM), and the relationship between kernel smoothness parameter of SVM and spectral separability are investigated with a limited number of sample. The overall accuracy (OA) for 8 classes was around 56.25% (kappa = 0.50) for MLC, 78.12 % (kappa=0.75) for SVM (radial basis function) and 78.90% (kappa=0.76) for SVM (polynomial). When the polynomial kernel increased from 2 to 4, producer accuracy (%) increased from 81.25% to 87.50% and 53.22% to 66.67 % for Mangrove (Avicennia marina) and Swamp She-oak (Casuarina glauca) tree species respectively. This accuracy is acceptable as 15% of the required sample provided 79% overall accuracy from SVM and is comparable to other previous studies.

    Original languageEnglish
    Title of host publicationIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
    Subtitle of host publicationproceedings
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages2674-2677
    Number of pages4
    ISBN (Electronic)9781538691540, 9781538691533
    ISBN (Print)9781538691557
    DOIs
    Publication statusPublished - 2019
    Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
    Duration: 28 Jul 20192 Aug 2019

    Publication series

    Name
    ISSN (Print)2153-6996
    ISSN (Electronic)2153-7003

    Conference

    Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
    CountryJapan
    CityYokohama
    Period28/07/192/08/19

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