CNN BASED SUBURBAN BUILDING DETECTION USING MONOCULAR HIGH RESOLUTION GOOGLE EARTH IMAGES

This paper proposes a deep convolutional neural networks (CNNs) based method to automatically detect suburban buildings from high resolution Google Earth imagery. Traditional methods based on low-level hand-engineered features or mid-level bag of features have great limitations in complex environment, especially in suburban areas. Inspired by the astounding achievement of CNNs in object recognition and detection, we develop a novel method to detect buildings in cluttered images which consists of three main steps. Firstly, a multi-scale saliency computation is employed to extract built-up areas and a sliding windows approach is applied to generate candidate regions. Then, a CNN is applied to classify the regions. Finally, an improved non maximum suppression is used to remove false buildings. We test our method on a collection of very challenging Google Earth images and achieve 89% precision, which shows robustness and efficiency of our method.