Dictionary Construction Method for Hyperspectral Remote Sensing Correlation Imaging

  • Qi Wang
  • Lingling MaEmail author
  • Hong Xu
  • Yongsheng Zhou
  • Chuanrong Li
  • Lingli Tang
  • Xinhong Wang
Part of the Springer Series in Optical Sciences book series (SSOS, volume 223)


The correlation imaging technique is a novel imaging strategy which acquires the object image by the correlation reconstruction algorithm from the separated signal light field and reference light field, with the advantages such as super-resolution, anti-interference and high security. The hyperspectral remote sensing correlation imaging technique combined the correlation imaging and hyperspectral remote sensing has the ability to detect the spectral properties of ground objects more than the above advantages. The spatial and spectral images can be reconstructed from very few measurements acquired by hyperspectral correlation imaging systems via sparsity constraint, making it an effective approach to solve the process and transition problem in high spatial and spectral resolution. While in the hyperspectral remote sensing correlation imaging, due to the complexity and variance of the spatial and spectral properties of the target scene and lack of prior knowledge, it is difficult to construct an effective dictionary for the hyperspectral reconstruction. The fixed dictionaries such as DCT (Discrete Cosine Transform) dictionary and wavelet dictionary are mainly used in the reconstruction up to present, and these dictionaries contain fixed and limited characteristics, which is hard to present different hyperspectral scenes efficiently. This paper aims at the problem that in the hyperspectral remote sensing correlation imaging system the sparse representation of complex ground objects is difficult in image reconstruction, resulting in low quality of reconstructed images. By combining the sparse coding and dictionary learning theory of signal processing, a related research on the construction method of hyperspectral remote sensing sparse dictionaries is carried out. Through the construction of hyperspectral remote sensing sparse dictionaries, optimization in reconstruction, and application research in actual imaging systems, a set of sparse dictionary construction and usage methods in hyperspectral correlation imaging has been formed. Comparing with current methods such as the total variation constraints and the hyperspectral image kernel norm constraints, the hyperspectral remote sensing scene sparsity and hyperspectral image reconstruction quality have been effectively improved by the proposed method, which has guiding significance for the development of the correlation imaging field.


Correlation imaging hyperspectral imaging Sparse representation Dictionary learning Reconstruction algorithm 



This work is supported in part by the National High Technology Research and Development Program of China under Grant 2014AA123201 and the National Key Research and Development Program of China under Grant 2016YFB0500402.


  1. 1.
    T.B. Pittman et al., Optical imaging by means of two-photon quantum entanglement. Phys. Rev. A 52(5), R3429–R3432 (1995)ADSCrossRefGoogle Scholar
  2. 2.
    A. Gatti et al., Coherent imaging with pseudo-thermal incoherent light. J. Mod. Opt. 53(5-6), 739–760 (2006)ADSCrossRefGoogle Scholar
  3. 3.
    Y. Yan, H. Dai, X. Liu, W. He, Q. Chen, G. Gu, Colored adaptive compressed imaging with a single photodiode. Appl. Opt. 55(14), 3711 (2016)ADSCrossRefGoogle Scholar
  4. 4.
    R. Boyd, Promises and Challenges of Ghost Imaging, in Signal Recovery and Synthesis (2011)Google Scholar
  5. 5.
    W. Gong, S. Han, Super-resolution ghost imaging via compressive sampling reconstruction. Physics (2009)Google Scholar
  6. 6.
    R.E. Meyers, K.S. Deacon, Y. Shih, Turbulence-free ghost imaging. Appl. Phys. Lett. 98(11), 111115–111115-3 (2011)ADSCrossRefGoogle Scholar
  7. 7.
    S. Yuan, X. Liu, X. Zhou, et al., Multiple-object ghost imaging with a single-pixel detector. J. Opt. 1–7 (2015)Google Scholar
  8. 8.
    Clemente P, Durán V, Torrescompany V, et al., Optical encryption based on computational ghost imaging. Opt. Lett. 35(14), 2391–2393 (2010)ADSCrossRefGoogle Scholar
  9. 9.
    J.M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, J. Chanussot, Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Mag. 1(2), 6–36 (2013)CrossRefGoogle Scholar
  10. 10.
    G. Brida, M.V. Chekhova, G.A. Fornaro, M. Genovese, E.D. Lopaeva, I.R. Berchera, Systematic analysis of signal-to-noise ratio in bipartite ghost imaging with classical and quantum light. Phys. Rev. A 83(6), 63807 (2011)ADSCrossRefGoogle Scholar
  11. 11.
    V.K.J. Astola, Compressive sensing computational ghost imaging. J. Opt. Soc. Am. A: 29(8), 1556–1567 (2012)ADSCrossRefGoogle Scholar
  12. 12.
    E.J. Candes, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Y. Song, The application of compressed sensing algorithm based on total variation method into ghost image reconstruction, in International Conference on Optoelectronics and Microelectronics Technology and Application, p. 102440X (2017)Google Scholar
  14. 14.
    L. Zhang, Y. Zhang, W. Wei, 3D total variation hyperspectral compressive sensing using unmixing. Geosci. Remote Sens. Sympos. (IGARSS) 2014, 2961–2964 (2014)Google Scholar
  15. 15.
    F. Yan, C.Y. JiaYingbiao et al., Compressed sensing projection and compound regularizer reconstruction for hyperspectral images. Acta Aeronautica et Astronautica Sinica 33(8), 1466–1473 (2012)Google Scholar
  16. 16.
    M. Golbabaee, P. Vandergheynst, Compressed sensing of simultaneous low-rank and joint-sparse matrices. IEEE Transac. Inf. Theor. (2012)Google Scholar
  17. 17.
    F.Y. JiaYingbiao, W. Zhongliang et al., Hyperspectral compressive sensing recovery via spectrum structure similarity. J. Electron. Inf. Technol. 6, 1406–1412 (2014)Google Scholar
  18. 18.
    K. Kreutz-Delgado, J.F. Murray, B.D. Rao et al., Dictionary learning algorithms for sparse representation. Neural Comput. 15(2), 349–396 (2014)zbMATHCrossRefGoogle Scholar
  19. 19.
    M.F. Duarte, M.A. Davenport, M.B. Wakin, J.N. Laska, D. Takhar, K.F. Kelly, et al., Multiscale random projections for compressive classification. IEEE Int. Conf. Image Process. 6, VI-161–VI-164Google Scholar
  20. 20.
    J. Wright, A. Ganesh, Z. Zhou, A. Wagner, Y. Ma, Demo: Robust face recognition via sparse representation. IEEE Int. Conf. Autom. Face Gesture Recog. 31, 1–2 (2009)Google Scholar
  21. 21.
    L.W. Kang, C.Y. Hsu, H.W. Chen, C.S. Lu, Secure SIFT-based sparse representation for image copy detection and recognition. IEEE Int. Conf. Multimedia Expo, 1248–1253 (2010)Google Scholar
  22. 22.
    K. Estabridis, Automatic target recognition via sparse representations, 7696(6), 701–712 (2010)Google Scholar
  23. 23.
    Q. Wang, H. Xu, L. Ma, et al., Hyperspectral compressive sensing imaging via spectral sparse constraint. In: International Conference on Photonics, Optics and Laser Technology (2018)Google Scholar
  24. 24.
    S.G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)zbMATHADSCrossRefGoogle Scholar
  25. 25.
    J. Tropp, A.C. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    D. Needell, R. Vershynin, Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Found. Comput. Math. 9(3), 317–334 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    D.L. Donoho, Y. Tsaig, I. Drori, J.L. Starck, Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Trans. Inf. Theory 58(2), 1094–1121 (2012)MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    J. Deng, G. Ren, Y. Jin, W. Ning, Iterative weighted gradient projection for sparse reconstruction. Inf. Technol. J. 10(7), 1409–1414 (2011)CrossRefGoogle Scholar
  29. 29.
    H. Mohimani, M. Babaie-Zadeh, C. Jutten, A fast approach for overcomplete sparse decomposition based on smoothed l 0 norm. IEEE Press. (2009)Google Scholar
  30. 30.
    P. Yang, F. Yan, F. Yang, Sparse array synthesis with regularized focuss algorithm, vol. 9, no. 6, pp. 1406–1407 (2013)Google Scholar
  31. 31.
    M.J. Lai, Y. Xu, W. Yin, Improved iteratively reweighted least squares for unconstrained smoothed ℓ q minimization. Siam J. Numer. Anal. 51(2), 927–957 (2013)MathSciNetzbMATHCrossRefGoogle Scholar
  32. 32.
    R. Rubinstein, T. Peleg, M. Elad, Analysis k-svd: a dictionary-learning algorithm for the analysis sparse model. IEEE Trans. Signal Process. 61(3), 661–677 (2013)MathSciNetzbMATHADSCrossRefGoogle Scholar
  33. 33.
    J. Wu, S. Xia, Y. Hong, C. Zhe, Z. Liu, S. Tan et al., Snapshot compressive imaging by phase modulation. Acta Optica Sinica 34(10), 113–120 (2014)Google Scholar
  34. 34.
    C. Li, An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing. Dissertations & Theses–Gradworks (2010)Google Scholar
  35. 35.
    S. Tan, Z. Liu, E. Li, S. Han, Hyperspectral compressed sensing based on prior images constrained. Acta Optica Sinica 35(8), 112–120 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Qi Wang
    • 1
  • Lingling Ma
    • 1
    Email author
  • Hong Xu
    • 1
  • Yongsheng Zhou
    • 1
  • Chuanrong Li
    • 1
  • Lingli Tang
    • 1
  • Xinhong Wang
    • 1
  1. 1.Key Laboratory of Quantitative Remote Sensing Information TechnologyAcademy of Opto-Electronics, Chinese Academy of SciencesBeijingChina

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