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.


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    Title :

    Classifying natural aerial scenery for autonomous aircraft emergency landing


    Contributors:


    Publication date :

    2014-05-01


    Size :

    2492099 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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