Onboard Hyperspectral Image Compression Using Compressed Sensing and Deep Learning

  • Saurabh KumarEmail author
  • Subhasis Chaudhuri
  • Biplab Banerjee
  • Feroz Ali
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11130)


We propose a real-time onboard compression scheme for hyperspectral datacube which consists of a very low complexity encoder and a deep learning based parallel decoder architecture for fast decompression. The encoder creates a set of coded snapshots from a given datacube using a measurement code matrix. The decoder decompresses the coded snapshots by using a sparse recovery algorithm. We solve this sparse recovery problem using a deep neural network for fast reconstruction. We present experimental results which demonstrate that our technique performs very well in terms of quality of reconstruction and in terms of computational requirements compared to other transform based techniques with some tradeoff in PSNR. The proposed technique also enables faster inference in compressed domain, suitable for on-board requirements.


Fast Hyperspectral imaging Fast on-board data compression Compressed sensing Deep learning 


  1. 1.
    Adão, T., et al.: Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 9, 1110 (2017)CrossRefGoogle Scholar
  2. 2.
    Benediktsson, J., Kanellopoulos, I.: Classification of multisource and hyperspectral data based on decision fusion. IEEE Trans. Geosci. Remote Sens. 37, 1367–1377 (1999)CrossRefGoogle Scholar
  3. 3.
    Du, Q., Fowler, J.: Hyperspectral image compression using JPEG2000 and principal component analysis. IEEE Geosci. Remote Sens. Lett. 4, 201–205 (2007)CrossRefGoogle Scholar
  4. 4.
    Foster, D., Amano, K., Nascimento, S., Foster, M.: Frequency of metamerism in natural scenes. JOSA 23, 2359–2372 (2006)CrossRefGoogle Scholar
  5. 5.
    Fowler, J., Rucker, J.: Three-dimensional wavelet-based compression of hyperspectral imagery. In: Hyperspectral Data Exploitation: Theory and Applications, pp. 379–407 (2007)Google Scholar
  6. 6.
    Friedl, M., Brodley, C.: Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 61, 399–409 (1997)CrossRefGoogle Scholar
  7. 7.
    Fu, W., Li, S., Fang, L., Benediktsson, J.A.: Adaptive spectral-spatial compression of hyperspectral image with sparse representation. IEEE Trans. Geosci. Remote Sens. 55(2), 671–682 (2017)CrossRefGoogle Scholar
  8. 8.
    Green, A., Berman, M., Switzer, P., Craig, M.: A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sens. 26, 65–74 (1988)CrossRefGoogle Scholar
  9. 9.
    Hitomi, Y., Gu, J., Gupta, M., Mitsunaga, T., Nayar, S.: Video from a single coded exposure photograph using a learned over-complete dictionary. In: International Conference on Computer Vision, pp. 287–294. IEEE (2011)Google Scholar
  10. 10.
    Iliadis, M., Spinoulas, L., Katsaggelos, A.: Deep fully-connected networks for video compressive sensing. Digit. Sig. Process. 72, 9–18 (2018)CrossRefGoogle Scholar
  11. 11.
    Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., Ashok, A.: Reconnet: non-iterative reconstruction of images from compressively sensed measurements. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 449–458 (2016)Google Scholar
  12. 12.
    Likas, A., Vlassis, N., Verbeek, J.: The global k-means clustering algorithm. Pattern Recogn. 36, 451–461 (2003)CrossRefGoogle Scholar
  13. 13.
    Rucker, J., Fowler, J., Younan, N.: JPEG2000 coding strategies for hyperspectral data. In: Geoscience and Remote Sensing Symposium, vol. 1. IEEE (2005)Google Scholar
  14. 14.
    Tang, X., Cho, S., Pearlman, W.: 3D set partitioning coding methods in hyperspectral image compression. In: International Conference on Image Processing, vol. 2, p. 239. IEEE (2003)Google Scholar
  15. 15.
    Tang, X., Pearlman, W., Modestino, J.: Hyperspectral image compression using three-dimensional wavelet coding. In: Image and Video Communications and Processing, vol. 5022, pp. 1037–1048. International Society for Optics and Photonics (2003)Google Scholar
  16. 16.
    Tropp, J., Gilbert, A.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53, 4655–4666 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wang, J., Chang, C.: Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Trans. Geosci. Remote Sens. 44, 1586–1600 (2006)CrossRefGoogle Scholar
  18. 18.
    Yang, J., et al.: Video compressive sensing using Gaussian mixture models. IEEE Trans. Image Process. 23(11), 4863–4878 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology BombayMumbaiIndia

Personalised recommendations