access icon free Sparsity measure based library aided unmixing of hyperspectral image

© The Institution of Engineering and Technology
Received 30/04/2018, Accepted 23/04/2019, Revised 22/10/2018, Published 26/04/2019
Loading full text...

Full text loading...

/deliver/fulltext/iet-ipr/13/12/IET-IPR.2018.5426.html;jsessionid=36o9qei7qki9d.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fiet-ipr.2018.5426&mimeType=html&fmt=ahah

Inspec keywords: learning (artificial intelligence); geophysical image processing; iterative methods; sparse matrices; image representation; image classification; hyperspectral imaging; remote sensing; feature extraction

Other keywords: sparsity measure; norm sparsity; remote sensing; pruned library; sparse inversion method; synthetic image datasets; sparse abundance matrix; parameter-free algorithm; library pruning; semiblind unmixing; highly coherent spectral library; abundance calculation task; computational efficiency; real image datasets; desirable sparsity properties; signal processing community; image endmembers; hyperspectral image; sparsity level; standard sparsity measures; library aided unmixing; application-specific spectral libraries; proficiency; abundance computation

Subjects: Linear algebra (numerical analysis); Knowledge engineering techniques; Geography and cartography computing; Other topics in statistics; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Interpolation and function approximation (numerical analysis); Linear algebra (numerical analysis); Image recognition; Other topics in Earth sciences; Other topics in statistics

References

    1. 1)
      • 31. Luo, B., Chanussot, J., Douté, S., et al: ‘Empirical automatic estimation of the number of endmembers in hyperspectral images’, IEEE Geosci. Remote Sens. Lett., 2013, 10, (1), pp. 24–28.
    2. 2)
      • 35. Sumarsono, A., Du, Q.: ‘Low-rank subspace representation for estimating the number of signal subspaces in hyperspectral imagery’, IEEE Trans. Geosci. Remote Sens., 2015, 53, (11), pp. 6286–6292.
    3. 3)
      • 18. Iordache, M.-D., Bioucas-Dias, J.M., Plaza, A., et al: ‘Music-csr: hyperspectral unmixing via multiple signal classification and collaborative sparse regression’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (7), pp. 4364–4382.
    4. 4)
      • 23. Tropp, J.A., Gilbert, A.C.: ‘Signal recovery from random measurements via orthogonal matching pursuit’, IEEE Trans. Inf. Theory, 2007, 53, (12), pp. 4655–4666.
    5. 5)
      • 40. Rasti, B., Ulfarsson, M.O., Sveinsson, J.R.: ‘Hyperspectral subspace identification using sure’, IEEE Geosci. Remote Sens. Lett., 2015, 12, (12), pp. 2481–2485.
    6. 6)
      • 38. Chang, C.-I., Xiong, W., Chen, H.-M., et al: ‘Maximum orthogonal subspace projection approach to estimating the number of spectral signal sources in hyperspectral imagery’, IEEE. J. Sel. Top. Signal. Process., 2011, 5, (3), pp. 504–520.
    7. 7)
      • 27. Tang, W., Shi, Z., Wu, Y., et al: ‘Sparse unmixing of hyperspectral data using spectral a priori information’, IEEE Trans. Geosci. Remote Sens., 2015, 53, (2), pp. 770–783.
    8. 8)
      • 6. Bioucas-Dias, J.M., Figueiredo, M.A.: ‘Alternating direction algorithms for constrained sparse regression: application to hyperspectral unmixing’. 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland, 2010, pp. 1–4.
    9. 9)
      • 37. Hansen, M.H., Yu, B.: ‘Model selection and the principle of minimum description length’, J. Am. Stat. Assoc., 2001, 96, (454), pp. 746–774.
    10. 10)
      • 26. Das, S., Routray, A., Deb, A.K.: ‘Hyperspectral unmixing by nuclear norm difference maximization based dictionary pruning’, arXiv preprint arXiv:1806.00864, 2018.
    11. 11)
      • 21. Akhtar, N., Shafait, F., Mian, A.: ‘Futuristic greedy approach to sparse unmixing of hyperspectral data’, IEEE Trans. Geosci. Remote Sens., 2015, 53, (4), pp. 2157–2174.
    12. 12)
      • 41. Ambikapathi, A., Chan, T.-H., Chi, C.-Y.: ‘Convex geometry based estimation of number of endmembers in hyperspectral images’. 2012 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 2012, pp. 1233–1236.
    13. 13)
      • 34. Asadi, H., Seyfe, B.: ‘Source number estimation via entropy estimation of eigenvalues (eee) in Gaussian and non-Gaussian noise’, arXiv preprint arXiv:1311.6051, 2013.
    14. 14)
      • 42. Dai, W., Milenkovic, O.: ‘Subspace pursuit for compressive sensing signal reconstruction’, IEEE Trans. Inf. Theory, 2009, 55, (5), pp. 2230–2249.
    15. 15)
      • 32. Das, S., Kundu, J.N., Routray, A.: ‘Estimation of number of endmembers in a hyperspectral image using eigen thresholding’. 2015 Annual IEEE India Conf. (INDICON), New Delhi, India, 2015, pp. 1–5.
    16. 16)
      • 11. Shi, Z., Shi, T., Zhou, M., et al: ‘Collaborative sparse hyperspectral unmixing using l0 norm’, IEEE Trans. Geosci. Remote Sens., 2018, 6, (9), pp. 5495–5508.
    17. 17)
      • 25. Das, S., Routray, A., Deb, A.: ‘Fast semi-supervised unmixing of hyperspectral image by mutual coherence reduction and recursive PCA’, Remote Sens., 2018, 10, (7), p. 1106.
    18. 18)
      • 2. Chang, C.-I.: ‘Hyperspectral data processing: algorithm design and analysis’ (John Wiley & Sons, New Jersey, 2013).
    19. 19)
      • 19. Fu, X., Ma, W.-K., Bioucas-Dias, J.M., et al: ‘Semiblind hyperspectral unmixing in the presence of spectral library mismatches’, IEEE Trans. Geosci. Remote Sens., 2016, 54, (9), pp. 5171–5184.
    20. 20)
      • 10. Wang, R., Li, H., Liao, W., et al: ‘Centralized collaborative sparse unmixing for hyperspectral images’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2017, 10, (5), pp. 1949–1962.
    21. 21)
      • 15. Xu, X., Shi, Z.: ‘Multi-objective based spectral unmixing for hyperspectral images’, ISPRS J. Photogramm. Remote Sens., 2017, 124, pp. 54–69.
    22. 22)
      • 36. Akaike, H.: ‘A new look at the statistical model identification’, IEEE Trans. Autom. Control, 1974, 19, (6), pp. 716–723.
    23. 23)
      • 17. Zhang, S., Li, J., Li, H.-C., et al: ‘Spectral-spatial weighted sparse regression for hyperspectral image unmixing’, IEEE Trans. Geosci. Remote Sens., 2018, 56, (6), pp. 3265–3276.
    24. 24)
      • 22. Shi, Z., Tang, W., Duren, Z., et al: ‘Subspace matching pursuit for sparse unmixing of hyperspectral data’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (6), pp. 3256–3274.
    25. 25)
      • 12. Feng, R., Wang, L., Zhong, Y., et al: ‘Differentiable sparse unmixing based on Bregman divergence for hyperspectral remote sensing imagery’. 2017 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Fort Worth, USA, 2017, pp. 598–601.
    26. 26)
      • 8. Iordache, M.-D., Bioucas-Dias, J.M., Plaza, A.: ‘Collaborative sparse regression for hyperspectral unmixing’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (1), pp. 341–354.
    27. 27)
      • 1. Landgrebe, D.: ‘Hyperspectral image data analysis’, IEEE Signal Process. Mag., 2002, 19, (1), pp. 17–28.
    28. 28)
      • 9. Zhang, S., Li, J., Liu, K., et al: ‘Hyperspectral unmixing based on local collaborative sparse regression’, IEEE Geosci. Remote Sens. Lett., 2016, 13, (5), pp. 631–635.
    29. 29)
      • 20. Cai, T.T., Wang, L.: ‘Orthogonal matching pursuit for sparse signal recovery with noise’, IEEE Trans. Inf. Theory, 2011, 57, (7), pp. 4680–4688.
    30. 30)
      • 14. Wang, R., Li, H.-C., Liao, W., et al: ‘Double reweighted sparse regression for hyperspectral unmixing’. Geoscience and Remote Sensing Symp. (IGARSS), 2016 IEEE Int., Beijing, China, 2016, pp. 6986–6989.
    31. 31)
      • 4. Bioucas-Dias, J.M., Plaza, A., Dobigeon, N., et al: ‘Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2012, 5, (2), pp. 354–379.
    32. 32)
      • 39. Chang, C.-I., Xiong, W., Wen, C.-H.: ‘A theory of high-order statistics-based virtual dimensionality for hyperspectral imagery’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (1), pp. 188–208.
    33. 33)
      • 16. Zhang, S., Li, J., Wu, Z., et al: ‘Spatial discontinuity-weighted sparse unmixing of hyperspectral images’, IEEE Trans. Geosci. Remote Sens., 2018, 56, (10), pp. 1–13.
    34. 34)
      • 3. Ma, W.-K., Bioucas-Dias, J.M., Chan, T.-H., et al: ‘A signal processing perspective on hyperspectral unmixing: insights from remote sensing’, IEEE Signal Process. Mag., 2014, 31, (1), pp. 67–81.
    35. 35)
      • 7. Iordache, M.-D., Bioucas-Dias, J.M., Plaza, A.: ‘Total variation spatial regularization for sparse hyperspectral unmixing’, IEEE Trans. Geosci. Remote Sens., 2012, 50, (11), pp. 4484–4502.
    36. 36)
      • 28. Tang, W., Shi, Z., Wu, Y.: ‘Regularized simultaneous forward–backward greedy algorithm for sparse unmixing of hyperspectral data’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (9), pp. 5271–5288.
    37. 37)
      • 30. Chang, C.-I., Du, Q.: ‘Estimation of number of spectrally distinct signal sources in hyperspectral imagery’, IEEE Trans. Geosci. Remote Sens., 2004, 42, (3), pp. 608–619.
    38. 38)
      • 13. Zheng, C.Y., Li, H., Wang, Q., et al: ‘Reweighted sparse regression for hyperspectral unmixing’, IEEE Trans. Geosci. Remote Sens., 2016, 54, (1), pp. 479–488.
    39. 39)
      • 24. Chen, Y., Nasrabadi, N.M., Tran, T.D.: ‘Hyperspectral image classification using dictionary-based sparse representation’, IEEE Trans. Geosci. Remote Sens., 2011, 49, (10), pp. 3973–3985.
    40. 40)
      • 33. Das, S., Routray, A., Deb, A.K.: ‘Noise robust estimation of number of endmembers in a hyperspectral image by eigenvalue based gap index’. 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, USA, 2016, pp. 1–5.
    41. 41)
      • 5. Iordache, M.-D., Bioucas-Dias, J.M., Plaza, A.: ‘Sparse unmixing of hyperspectral data’, IEEE Trans. Geosci. Remote Sens., 2011, 49, (6), pp. 2014–2039.
    42. 42)
      • 29. Bioucas-Dias, J.M., Nascimento, J.M.: ‘Hyperspectral subspace identification’, IEEE Trans. Geosci. Remote Sens., 2008, 46, (8), pp. 2435–2445.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5426
Loading

Related content

content/journals/10.1049/iet-ipr.2018.5426
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading