Building Detection from Very High Resolution Remotely Sensed Imagery Using Deep Neural Networks

Abstract

The past decades have witnessed a significant change in human societies with a fast pace and rapid urbanization. The boom of urbanization is contributed by the influx of people to the urban area and comes with building construction and deconstruction. The estimation of both residential and industrial buildings is important to reveal and demonstrate the human activities of the regions. As a result, it is essential to effectively and accurately detect the buildings in urban areas for urban planning and population monitoring. The automatic building detection method in remote sensing has always been a challenging task, because small targets cannot be identified in images with low resolution, as well as the complexity in the various scales, structure, and colours of urban buildings. However, the development of techniques improves the performance of the building detection task, by taking advantage of the accessibility of very high-resolution (VHR) remotely sensed images and the innovation of object detection methods. The purpose of this study is to develop a framework for the automatic detection of urban buildings from the VHR remotely sensed imagery at a large scale by using the state-of-art deep learning network. The thesis addresses the research gaps and difficulties as well as the achievements in building detection. The conventional hand-crafted methods, machine learning methods, and deep learning methods are reviewed and discussed. The proposed method employs a deep convolutional neural network (CNN) for building detection. Two input datasets with different spatial resolutions were used to train and validate the CNN model, and a testing dataset was used to evaluate the performance of the proposed building detection method. The experiment result indicates that the proposed method performs well at both building detection and outline segmentation task with a total precision of 0.92, a recall of 0.866, an F1-score of 0.891. In conclusion, this study proves the feasibility of CNN on solving building detection challenges using VHR remotely sensed imagery.