HF-FCN: Hierarchically Fused Fully Convolutional Network for Robust Building Extraction

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• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10111
• 期：1
• 页码：291-302
• 丛书名：Computer Vision ? ACCV 2016
• ISBN：978-3-319-54181-5
• 卷排序：10111

文摘

Automatic building extraction from remote sensing images plays an important role in a diverse range of applications. However, it is significantly challenging to extract arbitrary-size buildings with largely variant appearances or occlusions. In this paper, we propose a robust system employing a novel hierarchically fused fully convolutional network (HF-FCN), which effectively integrates the information generated from a group of neurons with multi-scale receptive fields. Our architecture takes an aerial image as the input without warping or cropping it and directly generates the building map. The experiment results tested on a public aerial imagery dataset demonstrate that our method surpasses state-of-the-art methods in the building detection accuracy and significantly reduces the time cost.