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Improved non-negative tensor Tucker decomposition algorithm for interference hyper-spectral image compression

改进的非负张量Tucker分解在干涉高光谱图像压缩的应用

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Abstract

The compression method, first proposed in 2012, is based on the non-negative tensor decomposition for interference hyper-spectral image data. As a tensor is generated by a huge amount of interference hyper-spectral images, the multiplicative update algorithm is made extremely complicated, and even unfeasible. To reduce the computational cost and speed up the convergence, this paper, based on the characteristics of interference hyper-spectral images, develops a new algorithm using different down-sampling factors for different non-negative wavelet sub-band tensors. The experimental results showed that this algorithm could significantly shorten the running time, while maintaining a good compression performance compared with the conventional methods.

摘要

创新点

基于非负张量分解的干涉高光谱图像压缩方法已经在2012年被提出, 但干涉高光谱图像的数据量巨大, 所形成的数据张量会非常大, 这会导致乘性迭代MU(Multiplicative Update)算法需要较高的计算复杂度, 甚至使得算法本身不可行。 为了减少计算代价和加快收敛速度, 本文针对干涉高光谱图像的光谱特性对不同的非负小波子带张量采用不同的下采样因子分别执行快速非负张量Tucker分解来实现数据压缩。 试验结果证实了基于下采样的多尺度非负张量逼近可以大大降低运行时间, 同时保持与未采样情形相当的压缩性能, 有效的提高了干涉高光谱图像压缩的实时性需求。

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Authors and Affiliations

  1. Science and Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Jia Wen & JunSuo Zhao

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, China

    Jia Wen & CaiWen Ma

  3. College of Computer Science, Xi’an Shiyou University, Xi’an, 710065, China

    CaiLing Wang

Authors
  1. Jia Wen
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  2. JunSuo Zhao
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  3. CaiWen Ma
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  4. CaiLing Wang
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Correspondence to Jia Wen.

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Wen, J., Zhao, J., Ma, C. et al. Improved non-negative tensor Tucker decomposition algorithm for interference hyper-spectral image compression. Sci. China Inf. Sci. 58, 1–9 (2015). https://doi.org/10.1007/s11432-014-5165-x

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  • DOI: https://doi.org/10.1007/s11432-014-5165-x

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