Development of a Building Detection System from an Aerial Image Based in Watershed Transformation and Linear Support Vector Machine
Object detection in an aerial image has always been a fundamental problem in remote sensing, more so with increasing population. With the advancement in sensor technology and falling prices in imaging hardware, it is now cheaper to acquire aerial images as compared to a decade ago. With increased quality and quantity of the images gathered, it is necessary to develop an automated object detection system to address tedious manual building detection. In this study, a two staged approach was executed to address automated building detection. First, we performed image segmentation to create meaningful regions of the image using a marker controlled watershed transform. Discrete Fourier Transform (DFT) coefficients were then derived from the grayscale histogram of each region to act as feature vector necessary for the next stage. Second, we trained linear support vector machines (SVM) using the acquired feature vector to identify the building and non-building regions of the test images. We evaluated the performance of the proposed method by using detection percentage, branching factor and receiver operating characteristic (ROC). We trained the linear SVM classifier with 872 building and 616 non-building images from 31 training images of the Calumpang aerial survey. Experimental results from 31 test images (of the same aerial survey) shows that the detection percentage and branching factor is 69.50% and 22.70%, respectively. Moreover, the area under the curve (AUC) of the ROC is 0.887 strongly suggesting that the proposed method is highly effective.
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