ZHAO Chunhui, LI Xiaohui, REN Jinchang, et al., “A Novel Framework for Object-Based Coding and Compression of Hyperspectral Imagery,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 300-305, 2015, doi: 10.1049/cje.2015.04.012
Citation: ZHAO Chunhui, LI Xiaohui, REN Jinchang, et al., “A Novel Framework for Object-Based Coding and Compression of Hyperspectral Imagery,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 300-305, 2015, doi: 10.1049/cje.2015.04.012

A Novel Framework for Object-Based Coding and Compression of Hyperspectral Imagery

doi: 10.1049/cje.2015.04.012
Funds:  This work is supported by the National Natural Science Foundation of China (No.61077079, No.61275010), the Key Program of Heilongjiang Natural Science Foundation (No.ZD201216), Program Excellent Academic Leaders of Harbin (No.RC2013XK009003) and the Fundamental Research Funds for the Central Universities (No.HEUCF1408).
  • Publish Date: 2015-04-10
  • A novel object-based framework is proposed for HSI compression, where targets of interest are extracted and separately coded. With objects removed, the holes are filled with the background average to form a new but more homogenous background for better compression. An improved sparse representation with adaptive spatial support is proposed for target detection. By applying the proposed framework to 2D/3D DCT approaches, reconstructed images from conventional and proposed approaches are compared. Six criteria in three groups are employed for quantitative evaluations to measure the degree of data reduction, the distortion of reconstructed image quality and accuracy in target detection, respectively. Comprehensive experiments on two datasets are used for performance evaluation. It is found that the proposed approaches yield much improved results.
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  • L. Tits, B. Sorners and P. Coppin, “The potential and limitations of a clustering approach for the improved efficiency of multiple endmember spectral mixture analysis in plant production system monitoring”, IEEE Transactions on Geoscience and Remote Sensing, Vol.50, No.6, pp.2273-2286, 2012.
    R.J. Murphy, S.T. Monteiro and S. Schneider, “Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors”, IEEE Transactions on Geoscience and Remote Sensing, Vol.50, No.8, pp.3066-3080, 2012.
    M.T. Eismann, A.D. Stocker and N. M. Naserbadi, “Automated hyperspectral cueing for civilian search and rescue”, Proceedings of the IEEE, Vol.97, No.6, pp.1031-1055, 2009.
    H. Chen, Y. Zhang, J. Zhang and Y. Chen, “A BOI-preservingbased compression method for hyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, Vol.48, No.11, pp.3913-3923, 2010.
    Y. Liang, J. Li and K. Guo, “Lossless compression of hyperspectral images using hybrid context prediction”, Optics Express, Vol.20, No.7, pp.8199-8206, 2012.
    S.-E. Qian, M. Bergeron, I. Cunningham, L. Gagnon and A. Hollonger, “Near lossless data compression onboard a hyperspectral satellite”, IEEE Transactions on Aerospace and Electronic Systems, Vol.42, No.3, pp.851-866, 2006.
    S.-E. Qian, “Fast vector quantization algorithms based on nearest partition set search”, IEEE Transactions on Image Processing, Vol.15, No.8, pp.2422-2430, 2006.
    I. Blanes and J. Serra-Sagrista, “Cost and scalability improvements to the Karhunen-Loêve transform for remote-sensing image coding”, IEEE Transactions on Geoscience and Remote Sensing, Vol.48, No.7, pp.2854-2863, 2010.
    A. Karami, M. Yazdi and G. Mercier, “Compression of hyperspectral images using discerete wavelet transform and tucker decomposition”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.5, No.2, pp.444-450, 2012.
    B. Penna, T. Tillo, E. Magli and G. Olmo, “Hyperspectral image compression employing a model of anomalous pixels”, IEEE Geoscience and Remote Sensing Letters, Vol.4, No.4, pp.664-668, 2007.
    D. Manolakis and G. Shaw, “Detection algorithm for hyperspectral imaging applications”, IEEE Signal Processing Magazine, Vol.19, No.1, pp.29-43, 2002.
    Y. Chen, N.M. Nasrabadi and T.D. Tran, “Sparse representation for target detection in hyperspectral imagery”, IEEE Journal of Selected Topics in Signal Processing, Vol.5, No.3, pp.629-640, 2011.
    D.A. Adjeroh and S.D. Sawant, “Error-resilient transmission for 3D DCT coded video”, IEEE Transactions on Broadcasting, Vol.55, No.2, pp.178-189, 2009.
    J. Mielikainen and P. Toivanen, “Clustered DPCM for the lossless compression of hyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, Vol.41, No.2, pp.2943-2946, 2003.
    M.W. Marcellin, P. Sriram and K.-L. Tong, “Transform coding of monochrome and color images using trellis coded quantization”, IEEE Transactions on Circuits and Systems for Video Technology, Vol.3, No.4, pp.270-276, 1993.
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