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


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).

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