Advertisement

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
  • 106 Downloads

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.

Keywords

Hyperspectral image Human detection Deep learning Object detection 

Notes

Compliance with ethical standards

Conflict of interest

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

References

  1. 1.
    Abrams, M.J., Ashley, R.P., Rowan, L.C., Goetz, A.F., Kahle, A.B.: Mapping of hydrothermal alteration in the Cuprite mining district, Nevada, using aircraft scanner images for the spectral region 0.46 to 2.36 µm. Geology 5(12), 713–718 (1977)ADSCrossRefGoogle Scholar
  2. 2.
    Zhang, C., Kovacs, J.M.: The application of small unmanned aerial systems for precision agriculture: a review. Precis. Agric. 13(6), 693–712 (2012)CrossRefGoogle Scholar
  3. 3.
    Liang, H.: Advances in multispectral and hyperspectral imaging for archaeology and art conservation. Appl. Phys. A 106(2), 309–323 (2012)ADSCrossRefGoogle Scholar
  4. 4.
    Edelman, G.J., Gaston, E., Van Leeuwen, T.G., Cullen, P.J., Aalders, M.C.G.: Hyperspectral imaging for non-contact analysis of forensic traces. Forensic Sci. Int. 223(1–3), 28–39 (2012)CrossRefGoogle Scholar
  5. 5.
    Lu, G., Fei, B.: Medical hyperspectral imaging: a review. Journal of biomedical optics 19(1), 010901 (2014)ADSCrossRefGoogle Scholar
  6. 6.
    Nasrabadi, N.M.: Hyperspectral target detection: an overview of current and future challenges. IEEE Signal Process. Mag. 31(1), 34–44 (2014)ADSCrossRefGoogle Scholar
  7. 7.
    Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004)ADSCrossRefGoogle Scholar
  8. 8.
    Mountrakis, G., Im, J., Ogole, C.: Support vector machines in remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 66(3), 247–259 (2011)ADSCrossRefGoogle Scholar
  9. 9.
    Belgiu, M., Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 114, 24–31 (2016)ADSCrossRefGoogle Scholar
  10. 10.
    Zhang, L., Zhang, L., Du, B.: Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geosci. Remote Sens. Mag. 4(2), 22–40 (2016)CrossRefGoogle Scholar
  11. 11.
    Pan, B., Shi, Z., Xu, X.: MugNet: deep learning for hyperspectral image classification using limited samples. ISPRS J. Photogramm. Remote Sens. 145, 108–119 (2018)ADSCrossRefGoogle Scholar
  12. 12.
    Pan, B., Shi, Z., Zhang, N., Xie, S.: Hyperspectral image classification based on nonlinear spectral–spatial network. IEEE Geosci. Remote Sens. Lett. 13(12), 1782–1786 (2016)ADSCrossRefGoogle Scholar
  13. 13.
    Hu, W., Huang, Y., Wei, L., Zhang, F., Li, H.: Deep convolutional neural networks for hyperspectral image classification. J. Sens. (2015).  https://doi.org/10.1155/2015/258619 CrossRefGoogle Scholar
  14. 14.
    Petersson, H., Gustafsson, D., Bergstrom, D.: Hyperspectral image analysis using deep learning—a review. In: Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on 1–6. IEEE (2016)Google Scholar
  15. 15.
    Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International 4959–4962 (2015)Google Scholar
  16. 16.
    Reed, I.S., Yu, X.: Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans Acoust Speech Signal Process 38(10), 1760–1770 (1990)ADSCrossRefGoogle Scholar
  17. 17.
    Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)Google Scholar
  18. 18.
    Ma, X., Wang, H., Geng, J.: Spectral–spatial classification of hyperspectral image based on deep auto-encoder. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(9), 4073–4085 (2016)ADSCrossRefGoogle Scholar
  19. 19.
    Manolakis, D., Shaw, G.: Detection algorithms for hyperspectral imaging applications. IEEE Signal Process. Mag. 19(1), 29–43 (2002)ADSCrossRefGoogle Scholar
  20. 20.
    Li, W., Wu, G., Du, Q.: transferred deep learning for anomaly detection in hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 14(5), 597–601 (2017)ADSCrossRefGoogle Scholar
  21. 21.
    Murray-Krezan, J., Neumann, J. G., & Leathers, R. A.: Small object hyperspectral detection from a low-flying UAV. In Signal and Data Processing of Small Targets 2008 (Vol. 6969, p. 69691C). International Society for Optics and Photonics. (2008)Google Scholar
  22. 22.
    Yan, L., Noro, N., Takara, Y., Ando, F., & Yamaguchi, M.: Using hyperspectral image enhancement method for small size object detection on the sea surface. In Image and Signal Processing for Remote Sensing XXI (Vol. 9643, p. 96430H). International Society for Optics and Photonics (2015)Google Scholar
  23. 23.
    Du, B., Zhang, Y., Zhang, L., Tao, D.: Beyond the sparsity-based target detector: A hybrid sparsity and statistics-based detector for hyperspectral images. IEEE Trans. Image Process. 25(11), 5345–5357 (2016)ADSMathSciNetCrossRefGoogle Scholar
  24. 24.
    Wang, T., Zhang, H., Lin, H., Jia, X.: A sparse representation method for a priori target signature optimization in hyperspectral target detection. IEEE Access 6, 3408–3424 (2018)CrossRefGoogle Scholar
  25. 25.
    Park, J.J., Oh, S., Park, K.A., Foucher, P.Y., Jang, J.C., Lee, M., Kang, W.S.: The Ship Detection Using Airborne and In-situ Measurements Based on Hyperspectral Remote Sensing. J. Korean Earth Sci. Soc. 38(7), 535–545 (2017)CrossRefGoogle Scholar
  26. 26.
    Salem M.B., Ettabaa K.S., Hamdi M.A.: Anomaly detection in hyperspectral imagery: an overview. Image Processing, Applications and Systems Conference. Sfax,105–13 (2014)Google Scholar
  27. 27.
    Chang, C.I., Jiao, X., Wu, C.C., Du, Y., Chang, M.L.: A review of unsupervised spectral target analysis for hyperspectral imagery. EURASIP J. Adv. Signal Process. 2010(1), 503752 (2010)CrossRefGoogle Scholar
  28. 28.
    Wang, Z., Yin, Q., Li, H., Hu, B.: Surface ship target detection in hyperspectral images based on improved variance minimum algorithm. In eighth international conference on digital image processing (ICDIP 2016). Int. Soc. Optics Photon. 10033, 100330R (2016)Google Scholar
  29. 29.
    Takara, Y., Manago, N., Saito, H., Mabuchi, Y., Kondoh, A., Fujimori, T., Kuze, H.: Remote sensing applications with NH hyperspectral portable video camera. In multispectral, hyperspectral, and ultraspectral remote sensing technology, techniques and applications IV. Int. Soc. Optics Photonics 8527, 85271G (2012)Google Scholar
  30. 30.
    François, C., et al.: GitHub Repository. https://keras.io (2015). Accessed 1 Nov 2018
  31. 31.
    Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J. , Kudlur, M.: Tensorflow: a system for large-scale machine learning. OSDI 16, 265–283) (2016).Google Scholar
  32. 32.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Vanderplas, J.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 12, 2825–2830 (2011).MathSciNetzbMATHGoogle Scholar
  33. 33.
    Chaudhury, S.: GitHub Repository. https://github.com/subhajitchaudhury/deephypercnn (2016). Accessed 1 Nov 2018

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

Personalised recommendations