Spaceborne monitoring of wide maritime areas can be suitable for many applications such as tracking of ship traffic, surveillance of fishery zones, or detecting criminal activities. We present novel approaches for segmentation and classification of man-made objects in TerraSAR-X images including estimation of orientation and size. This is a difficult task as detections are affected by clutter and noise effects, and each object can have different appearances. We chose a statistical approach to robustly segment given detections using Local Binary Pattern (LBP) and Histograms of Oriented Gradients (HOG). This is the fundament for subsequent feature analysis and 3-stage-classification based on Support Vector Machines (SVM) with separation of clutter and man-made objects in first, non-ships and ships in second, and different ship structure types in third stage. An experimental evaluation demonstrates the effective operation of our approaches.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Segmentation and classification of man-made maritime objects in TerraSAR-X images


    Beteiligte:
    Teutsch, Michael (Autor:in) / Saur, Günter (Autor:in)

    Kongress:

    2011


    Erscheinungsdatum :

    2011


    Format / Umfang :

    4 pages



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Detection and classification of offshore artificial objects in TerraSAR-X images: First outcomes of the DeMarine-DEKO project

    Estable, S. / Teufel, F. / Petersen, L. et al. | Fraunhofer Publica | 2009

    Freier Zugriff

    Feature extraction for man-made objects segmentation in aerial images

    Wei, W. / Xin, Y. | British Library Online Contents | 2008


    TerraSAR-X

    Fabrizio Bernardini | DOAJ | 2007

    Freier Zugriff

    TerraSAR-X

    Fabrizio Bernardini | DOAJ | 2007

    Freier Zugriff