Optical Review

, Volume 26, Issue 6, pp 597–606 | Cite as

A novel two-stage deep learning-based small-object detection using hyperspectral images

  • Lu YanEmail author
  • Masahiro Yamaguchi
  • Naoki Noro
  • Yohei Takara
  • Fuminori Ando
Regular Paper


Hyperspectral imaging has drawn significant attention in recent years, and its application to object detection and classification is currently an important research topic. However, finding a method to accurately identify objects that only occupy a very small part of an image area remains to be a challenge. In this paper, a novel two-stage deep learning-based hyperspectral neural network (2SHyperNet) suitable for human detection from the sea surface is proposed. The method combines spatial and spectral information of hyperspectral images. Pixel-wise spectral information is used in the first stage to obtain first-stage classification results, and then the results are combined with spatial information to help eliminate unlikely regions, thus, improving the detection accuracy. The method is tested on a data set of real-world airborne hyperspectral images, and its performance is compared with those of several conventional methods. The results show that the proposed method outperforms current state-of-the-art methods.


Hyperspectral image Human detection Deep learning Object detection 


Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


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Copyright information

© The Optical Society of Japan 2019

Authors and Affiliations

  1. 1.Department of Information Processing, Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of TechnologyYokohamaJapan
  2. 2.Department of Information and Communications Engineering, School of EngineeringTokyo Institute of TechnologyYokohamaJapan
  3. 3.EBA JAPAN CO., LTD.TokyoJapan

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