Novel Approach for Rooftop Detection Using Support Vector Machine

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.

Building Footprint Extraction From VHR Remote Sensing Images Combined With Normalized DSMs Using Fused Fully Convolutional Networks

“…The NDVI data extracted from red and near-infrared (NIR) channels of a multispectral image indicate vegetation and, as a result, can help to eliminate trees. The early approaches for image classification typically employ task-specific features like color histograms or local binary patterns and pass them to machine learning algorithms to generate a labeled image [20][21][22]. Ngo et al [23] decompose an image into small homogeneous regions, which are then grouped into clusters.…”

Advances in remote sensing applications for urban sustainability

“…Bergamasco and Asinari [6] computed the actual roof surface available for PV installations by classifying roof typologies. Baluyan et al [4] discriminated rooftops from non-rooftops based on colour/grey level during image segmentation, support vector machine (SVM) classification, and the histogram method.…”

Building Rooftops Extraction for Solar Pv Potential Estimation Using Gis-Based Methods

“…On the other hand, rooftops automatic extraction methods have been widely used instead of manual digitizing. These methods depend on data segmentation and using machine learning techniques to successfully extract the object of interest from different segments (Baluyan et al, 2013;Ghanea, Moallem and Momeni, 2014;Joshi et al, 2014). For instance, Ghanea, Moallem and Momeni (2014) used k-means clustering for segmentation, where k value was chosen to be 2 to get a binary image with the 'semi-building' and 'non-building' layers.…”

“…After that, region growing was used to form buildings and a decision tree classification algorithm was applied to divide the layers and extract the 'buildings' with an overall accuracy of 80%. Baluyan et al (2013) carried out some pre-processing algorithms, including bilateral filtering to remove the noise. Thus, the edges could be preserved for facilitating the segmentation process using k-means clustering Support vector machine (SVM) was then used to extract the buildings with a precision of 93%.…”