• Journal of Terahertz Science and Electronic Information Technology
  • Vol. 19, Issue 6, 1075 (2021)
SU Linghua1、*, WANG Ping2, MA Zhiqiang1, and ZHANG Qian1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    DOI: 10.11805/tkyda2020106 Cite this Article
    SU Linghua, WANG Ping, MA Zhiqiang, ZHANG Qian. Efficient compression of hyperspectral images based on spectral linear decomposition[J]. Journal of Terahertz Science and Electronic Information Technology , 2021, 19(6): 1075 Copy Citation Text show less
    References

    [4] CONOSCENTI M, COPPOLA R, MAGLI E. Constant SNR, rate control, and entropy coding for predictive lossy hyperspectral image compression[J]. IEEE Transactions on Image Processing, 2016, 54(12):7431-7441.

    [5] WANG Z L, FENG W T, NIAN Y J. Compressive-sensing-based lossy compression for hyperspectral images using spectral unmixing[J]. Infrared and Laser Engineering, 2018, 47(S1):S126003.

    [6] LI J, FU Y, LI G N, et al. Remote sensing image compression in visible/near-infrared range using heterogeneous compressive sensing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(12):4932-4938.

    [7] RUCKER J T, FOWLER J E, YOUNAN N H. JPEG2000 coding strategies for hyperspectral data[C]// Proceedings of the International Geoscience and Remote Sensing Symposium. Seoul, South Korea:[s.n.], 2005:128-131.

    [8] CAGNAZZO M, POGGI G, VERDOLIVA L. Region-based transform coding of multispectral images[J]. IEEE Transactions on Image Processing, 2007, 16(12):2916-2926.

    [10] BLANES I, SERRA-SAGRISTA J. Pairwise orthogonal transform for spectral image coding[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(3):961-972.

    [12] GUNDOGAR Z, TOREYIN B U, DEMIRALP M. Tridiagonal format enhanced multivariance products representation based hyperspectral data compression[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(9):3272-3278.

    [13] VALSESIA D, MAGLI E. High-throughput on board hyperspectral image compression with ground-based CNN reconstruction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12):9544-9553.

    [14] BARRIOS Y, SANCHEZ A J, SANTOS Lucana, et al. SHyLoC 2.0:a versatile hardware solution for on-board data and hyperspectral image compression on future space missions[J]. IEEE Access, 2020(8):54269-54287.

    [15] DU Q, CHANG C I. Linear mixture analysis-based compression for hyperspectal image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4):875-891.

    [18] BIOUCAS-Dias J M, NASCIMENTO J M P. Hyperspectral subspace identification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(8):2435-2445.

    SU Linghua, WANG Ping, MA Zhiqiang, ZHANG Qian. Efficient compression of hyperspectral images based on spectral linear decomposition[J]. Journal of Terahertz Science and Electronic Information Technology , 2021, 19(6): 1075
    Download Citation