Sparse logistic regression and polynomial modelling for detection of artificial drainage networks

Kaisa Liimatainen, Raimo Heikkilä, Olli Yli-Harja, Heikki Huttunen, Pekka Ruusuvuori

    Research output: Contribution to journalArticleScientificpeer-review

    4 Citations (Scopus)

    Abstract

    Mire ditching changes dramatically mire biodiversity. Thus, drainage network detection is an important factor when analysing the natural state of a mire. In this article, we propose a method for automated drainage network detection from raster digital terrain model created from high-resolution laser scanning data. Sparse logistic regression classifier with a large generic feature set and automated feature selection is used for classification. Broken segments are connected with polynomial modelling. The results showed that our method can accurately detect artificial drainage networks.

    Original languageEnglish
    Pages (from-to)311-320
    Number of pages10
    JournalRemote Sensing Letters
    Volume6
    Issue number4
    DOIs
    Publication statusPublished - 3 Apr 2015
    Publication typeA1 Journal article-refereed

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Earth and Planetary Sciences (miscellaneous)
    • Electrical and Electronic Engineering

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