Automatic rooftop detection using a two-stage classification

Bikash Joshi, Hayk Baluyan, Amer Al Hinai, Wei Lee Woon

نتاج البحث

5 اقتباسات (Scopus)

ملخص

This paper presents a novel application of machine learning techniques to the automatic detection of building rooftops in satellite images. The image is first segmented into homogeneous regions using the k-means algorithm. These segments are then treated as candidate rooftop regions which are presented to a novel two-stage classification process; features are extracted from each segment and submitted to an ANN which serves as the first stage of the classification procedure. New features are then extracted from the outputs of the ANN and these are presented to an SVM which then performs the second classification pass. In this way, the first classification stage acts as a preprocessing step which, when processed by the SVM significantly reduces the number of false-positives. To establish the efficacy of the proposed method, its results are compared with those obtained using an alternative approach.

اللغة الأصليةEnglish
عنوان منشور المضيفProceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014
المحررونJasmy Yunus, Richard Cant, Ismail Saad, David Al-Dabass, Zuwairie Ibrahim, Alessandra Orsoni
ناشرInstitute of Electrical and Electronics Engineers Inc.
الصفحات286-291
عدد الصفحات6
رقم المعيار الدولي للكتب (الإلكتروني)9781479949236
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2014
منشور خارجيًانعم
الحدث16th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2014 - Cambridge
المدة: مارس ٢٦ ٢٠١٤مارس ٢٨ ٢٠١٤

سلسلة المنشورات

الاسمProceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014

Other

Other16th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2014
الدولة/الإقليمUnited Kingdom
المدينةCambridge
المدة٣/٢٦/١٤٣/٢٨/١٤

ASJC Scopus subject areas

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