www.ijsret.org 549 International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 4, Issue 5, May 2015 Detection of Rooftop Regions in Rural Areas Using Support Vector Machine Liya Joseph 1 , Laya Devadas 2 1 (M Tech Scholar, Department of Computer Science, College of Engineering Munnar, Kerala) 2 (Associate Professor, Department of Computer Science, College of Engineering Munnar, Kerala) ABSTRACT Rooftop detection in rural areas is an important task in many applications including vegetation identification, land encroachment detection, route planning to rural areas etc. This paper proposes a new approach for rooftop detection using machine learning techniques. The rural area selected for this study is Munnar in Kerala. In the first step satellite images of Munnar are randomly collected from Google Maps. It consists of both rooftop and non-rooftop images. An initial Support Vector Machine (SVM) classifier is used to detect rooftop images. This rooftop image is segmented into different candidate rooftop regions using k-means clustering algorithm. Then each candidate region is given to a final Support Vector Machine classifier, which predicts the true rooftop candidate. The performance of this method is evaluated using Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The Results show that proposed method has lower value of MSE and higher value of PSNR compared to existing methods. Keywords – Feature extraction, Image segmentation, K means clustering, Machine learning techniques, Support Vector Machine 1. INTRODUCTION In recent years, there has been an increasing demand for rooftop detection due to its variety of applications. All these methods are mainly focus on urban area rooftop detection. But this paper proposes a new approach for rooftop detection in rural areas. In rural areas, the crimes like unofficial settlements in government land, land encroachment are increases day by day. To detect these crimes as well as for the other applications like vegetation identification, change detection, route planning to rural areas based on rooftop density, tourism development can be detected by this new approach. Section 2 describes related work, section 3 describes proposed method, section 4 describes results and discussions and section 5 concludes the paper. 2. RELATED WORK Rooftop detection is a tedious task, however many rooftop detection methods are exists. Most of the earlier work on rooftop detection has based on edge detection, corner detection, and image segmentation. Detection of building with polygonal shapes rooftops [1] is an edge and corner based detection technique. It is based on detecting lines and their intersections using a graph representation. Then find a polygonal shape in the graph which corresponds to loop in the graph. It detects only polygonal shaped rooftops. An automatic building extraction from remote sensing images [2] is based on both region growing and morphological methods. But this approach could not detect buildings with dark rooftops. Another method based on both edge detection and Hough transform algorithm [3]. The idea behind many modern approaches is machine learning techniques. Machine Learning deals with the construction and study of systems that can learn from data rather than follow any explicitly programmed instructions. In the proposed system it first generates rooftop candidates using image segmentation method and detects true rooftops from it using Artificial Neural Network [4] and SVM [5]. The existing methods focus only on detection of rooftops in urban area. But the proposed method mainly focus on detection of rooftops in rural area particularly Munnar at Idukki district in Kerala. Munnar is a hill station area with enchanting range of vegetation. The proposed method is capable of detecting rooftops in urban areas also. 3. PROPOSED METHOD Proposed rooftop detection system consists of following steps: