Table of Contents
ISRN Machine Vision
Volume 2013 (2013), Article ID 819768, 11 pages
http://dx.doi.org/10.1155/2013/819768
Research Article

Novel Approach for Rooftop Detection Using Support Vector Machine

Computing and Information Science, Masdar Institute of Science and Technology, Masdar City, Abu Dhabi, UAE

Received 26 September 2013; Accepted 21 November 2013

Academic Editors: A. Gasteratos, S.-J. Horng, J. M. Tavares, and C. S. Won

Copyright © 2013 Hayk Baluyan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A new method for detecting rooftops in satellite images is presented. The proposed method is based on a combination of machine learning techniques, namely, k-means clustering and support vector machines (SVM). Firstly k-means clustering is used to segment the image into a set of rooftop candidates—these are homogeneous regions in the image which are potentially associated with rooftop areas. Next, the candidates are submitted to a classification stage which determines which amongst them correspond to “true” rooftops. To achieve improved accuracy, a novel two-pass classification process is used. In the first pass, a trained SVM is used in the normal way to distinguish between rooftop and nonrooftop regions. However, this can be a challenging task, resulting in a relatively high rate of misclassification. Hence, the second pass, which we call the “histogram method,” was devised with the aim of detecting rooftops which were missed in the first pass. The performance of the model is assessed both in terms of the percentage of correctly classified candidates as well as the accuracy of the estimated rooftop area.