Automatic rooftop detection using a two-stage classification

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

doi:10.1109/UKSim.2014.89

Automatic rooftop detection using a two-stage classification

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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	286-291
Number of pages	6
ISBN (Electronic)	9781479949236
DOIs	https://doi.org/10.1109/UKSim.2014.89
Publication status	Published - 2014
Event	16th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2014 - Cambridge, United Kingdom Duration: Mar 26 2014 → Mar 28 2014

Other

Other	16th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2014
Country/Territory	United Kingdom
City	Cambridge
Period	3/26/14 → 3/28/14

Keywords

Artificial neural network
Computer vision
Image segmentation
Machine learning
Rooftop detection
Support vector machine

ASJC Scopus subject areas

Computational Theory and Mathematics
Applied Mathematics
Modelling and Simulation

Access to Document

10.1109/UKSim.2014.89

Cite this

Automatic rooftop detection using a two-stage classification. / Joshi, Bikash; Baluyan, Hayk; Al Hinai, Amer; Woon, Wei Lee.

Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 286-291 7046079.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Joshi, B, Baluyan, H, Al Hinai, A & Woon, WL 2014, Automatic rooftop detection using a two-stage classification. in Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014., 7046079, Institute of Electrical and Electronics Engineers Inc., pp. 286-291, 16th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2014, Cambridge, United Kingdom, 3/26/14. https://doi.org/10.1109/UKSim.2014.89

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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",

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AU - Woon, Wei Lee

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N2 - 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.

AB - 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.

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KW - Image segmentation

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Automatic rooftop detection using a two-stage classification

Abstract

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Keywords

ASJC Scopus subject areas

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