In this paper, we present an approach for image-based surface classification using multi-class Support Vector Machine (SVM). Classifying surfaces in aerial images is an important step towards an increased aircraft autonomy in emergency landing situations. We design a one-vs-all SVM classifier and conduct experiments on five data sets. Results demonstrate consistent overall performance figures over 88% and approximately 8% more accurate to those published on multi-class SVM on the KTH TIPS data set. We also show per-class performance values by using normalised confusion matrices. Our approach is designed to be executed online using a minimum set of feature attributes representing a feasible and ready-to-deploy system for onboard execution.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Classifying natural aerial scenery for autonomous aircraft emergency landing


    Beteiligte:
    Mejias, Luis (Autor:in)


    Erscheinungsdatum :

    2014-05-01


    Format / Umfang :

    2492099 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Unmanned aerial vehicle aircraft emergency active landing device

    ZHANG GUOLIANG / JI WEI / LIANG ZHONGDONG | Europäisches Patentamt | 2021

    Freier Zugriff

    Emergency landing of aircraft

    ROGER MARK SLOMAN | Europäisches Patentamt | 2022

    Freier Zugriff

    EMERGENCY LANDING OF AIRCRAFT

    SLOMAN ROGER MARK | Europäisches Patentamt | 2022

    Freier Zugriff

    EMERGENCY LANDING OF AIRCRAFT

    SLOMAN ROGER MARK | Europäisches Patentamt | 2020

    Freier Zugriff

    Emergency landing apparatus deployment for emergency landing of aircraft

    SLOMAN ROGER MARK | Europäisches Patentamt | 2022

    Freier Zugriff