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

  • Tongchun Zuo
  • Juntao Feng
  • Xuejin ChenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10111)

Abstract

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.

Keywords

Local Binary Pattern Conditional Random Field Aerial Image Variant Appearance Convolutional Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

We would like to thank the anonymous reviewers. This work was supported by the National Natural Science Foundation of China (NSFC) under Nos. 61472377 and 61331017, and the Fundamental Research Funds for the Central Universities under No. WK2100060011.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application SystemUniversity of Science and Technology of ChinaHefeiChina

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