@inproceedings{7b464f11ff7f4371a2673c3f8a46d1ef,
title = "Automatic rooftop detection using a two-stage classification",
abstract = "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.",
keywords = "Artificial neural network, Computer vision, Image segmentation, Machine learning, Rooftop detection, Support vector machine",
author = "Bikash Joshi and Hayk Baluyan and {Al Hinai}, Amer and Woon, {Wei Lee}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 16th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2014 ; Conference date: 26-03-2014 Through 28-03-2014",
year = "2014",
doi = "10.1109/UKSim.2014.89",
language = "English",
series = "Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "286--291",
editor = "Jasmy Yunus and Richard Cant and Ismail Saad and David Al-Dabass and Zuwairie Ibrahim and Alessandra Orsoni",
booktitle = "Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014",
}