• Laser & Optoelectronics Progress
  • Vol. 57, Issue 14, 141032 (2020)
Li Wang*, Wei Wang**, and Boni Liu***
Author Affiliations
  • Department of Electronic Engineering, Xi'an Aeronautical University, Xi'an, Shaanxi 710077, China
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    DOI: 10.3788/LOP57.141032 Cite this Article Set citation alerts
    Li Wang, Wei Wang, Boni Liu. Hermitian Compressed Sensing Reconstruction Algorithm for Hyperspectral Images[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141032 Copy Citation Text show less
    References

    [1] Cai Q K, Li E J, Jiang J B et al. Study on the tea identification of near-infrared hyperspectral image combining spectra-spatial information[J]. Spectroscopy and Spectral Analysis, 39, 2522-2527(2019).

    [2] Niu Y B, Wang B. Extracting target spectrum for hyperspectral target detection: an adaptive weighted learning method using a self-completed background dictionary[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 1604-1617(2017).

    [3] Chen S X, Zhou Y F, Qi R L. Joint sparse representation of hyperspectral image classification based on kernel function[J]. Systems Engineering and Electronics, 40, 692-698(2018).

    [4] Liu L X, Li M Z, Zhao Z G et al. Recent advances of hyperspectral imaging application in biomedicine[J]. Chinese Journal of Lasers, 45, 0207017(2018).

    [5] Chang C I. A review of virtual dimensionality for hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 1285-1305(2018).

    [6] AlSuwaidi A, Grieve B, Yin H J. Feature-ensemble-based novelty detection for analyzing plant hyperspectral datasets[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 1041-1055(2018).

    [7] Tang Z Q, Fu G Y, Chen J et al. Low-rank structure based hyperspectral compression representation[J]. Journal of Electronics & Information Technology, 38, 1085-1091(2016).

    [8] Huang D M, Zhang X T, Zhang M H et al. Feature extraction of hyperspectral images based on semi-supervised locality preserving projection with spatial-correlation[J]. Laser & Optoelectronics Progress, 56, 021003(2019).

    [9] Xu M E, Xie B L, Xu G M. Hyperspectral image super-resolution method based on spatial spectral joint sparse representation[J]. Laser & Optoelectronics Progress, 55, 071014(2018).

    [10] Sun B M, Guo Y, Li N et al. An efficient counting and localization framework for off-grid targets in WSNs[J]. IEEE Communications Letters, 21, 809-812(2017).

    [11] Wang Y Y, Ren Y C, Chen L Y et al. Terahertz wave wide-beam imaging technology based on block compressive sensing theory[J]. Acta Optica Sinica, 39, 0411008(2019).

    [12] Yu D P, Guo Y, Li N et al. Dictionary refinement method for compressive sensing based multi-target device-free localization[J]. Journal of Electronics & Information Technology, 41, 865-871(2019).

    [13] Jiang Y, Wang B Q, Han J et al. Underdetermined wideband DOA estimation based on distributed compressive sensing[J]. Journal of Electronics & Information Technology, 41, 1690-1697(2019).

    [14] Yin J H, Sun J Y, Jia X P. Sparse analysis based on generalized Gaussian model for spectrum recovery with compressed sensing theory[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2752-2759(2015).

    [15] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 53, 4655-4666(2007).

    [16] Liu H L, Wang C J, Chen Y. FBG spectral compression and reconstruction method based on segmented adaptive sampling compressed sensing[J]. Chinese Journal of Lasers, 45, 0306004(2018).

    [17] Sun Y B, Wu Z B, Wu M et al. Compressed sensing reconstruction of hyperspectral imagery jointly using low rank and sparse prior[J]. Acta Electronica Sinica, 42, 2219-2224(2014).

    [18] Fowler J E, Du Q. Reconstructions from compressive random projections of hyperspectral imagery[M]. ∥Optical Remote Sensing. Berlin, Heidelberg: Springer Berlin Heidelberg, 31-48(2011).

    [19] Xu Y, Wu Z B, Chanussot J et al. Joint reconstruction and anomaly detection from compressive hyperspectral images using mahalanobis distance-regularized tensor RPCA[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 2919-2930(2018).

    [20] Wang L, Feng Y. Compressed sensing reconstruction of hyperspectral images based on spatial-spectral multihypothesis prediction[J]. Journal of Electronics & Information Technology, 37, 3000-3008(2015).

    [21] Wang Z L, Feng Y, Jia Y B. Spatio-spectral hybrid compressive sensing of hyperspectral imagery[J]. Remote Sensing Letters, 6, 199-208(2015).

    [22] Wang L, Feng Y. Sparse decomposition of images based on particle swarm optimization[J]. Computer Simulation, 32, 363-367(2015).

    [23] Wang L, Wang W, Chen B. Improved particle swarm optimization orthogonal matching pursuit reconstruction algorithm[J]. Journal of Chinese Computer Systems, 40, 1755-1759(2019).

    [24] Cao J S. Space-time adaptive processing algorithms for airborne phase-array radar[D]. Chengdu: University of Electronic Science and Technology of China(2007).

    [25] Li X L. A new intelligent optimization method-artificial fish school algorithm[D]. Hangzhou: Zhejiang University(2003).

    Li Wang, Wei Wang, Boni Liu. Hermitian Compressed Sensing Reconstruction Algorithm for Hyperspectral Images[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141032
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