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

Abstract

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.

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Correspondence to Lu Yan.

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Yan, L., Yamaguchi, M., Noro, N. et al. A novel two-stage deep learning-based small-object detection using hyperspectral images. Opt Rev 26, 597–606 (2019). https://doi.org/10.1007/s10043-019-00528-0

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  • DOI: https://doi.org/10.1007/s10043-019-00528-0

Keywords

  • Hyperspectral image
  • Human detection
  • Deep learning
  • Object detection