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

作　　者：WEN Jia ZHAO JunSuo MA CaiWen WANG CaiLing 

机构地区：[1]Science and Technology on Integrated Information System Laboratory, Institute of Software,Chinese Academy of Sciences [2]Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences [3]College of Computer Science, Xi'an Shiyou University

出　　处：《Science China(Information Sciences)》2015年第5期108-116,共9页中国科学（信息科学）（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.61401439,41301382and 61202218);the National High Technology Research and Development Program of China(863 Program)(Grant No.2012AA011206)

摘　　要：The compression method, first proposed in 2012, is based on the non-negative tensor decompo- sition 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.The compression method, first proposed in 2012, is based on the non-negative tensor decompo- sition 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.

关 键 词：interference hyper-spectral images LASIS three-dimensional lifting wavelet transform non-negative tensor decomposition image compression 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]