Multiple object extraction from aerial imagery with convolutional neural networks

Shunta Saito, Takayoshi Yamashita, Yoshimitsu Aoki

研究成果: Conference article査読

73 被引用数 (Scopus)

抄録

An automatic system to extract terrestrial objects from aerial imagery has many applications in a wide range of areas. However, in general, this task has been performed by human experts manually, so that it is very costly and time consuming. There have been many attempts at automating this task, but many of the existing works are based on class-specific features and classifiers. In this article, the authors propose a convolutional neural network (CNN)-based building and road extraction system. This takes raw pixel values in aerial imagery as input and outputs predicted three-channel label images (building-road-background). Using CNNs, both feature extractors and classifiers are automatically constructed. The authors propose a new technique to train a single CNN efficiently for extracting multiple kinds of objects simultaneously. Finally, they show that the proposed technique improves the prediction performance and surpasses state-of-the-art results tested on a publicly available aerial imagery dataset.

本文言語English
ジャーナルIS and T International Symposium on Electronic Imaging Science and Technology
DOI
出版ステータスPublished - 2016
イベント33rd Intelligent Robots and Computer Vision: Algorithms and Techniques Conference - San Francisco, United States
継続期間: 2016 2月 142016 2月 18

ASJC Scopus subject areas

  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • コンピュータ サイエンスの応用
  • 人間とコンピュータの相互作用
  • ソフトウェア
  • 電子工学および電気工学
  • 原子分子物理学および光学

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