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Soft Computing

, Volume 22, Issue 10, pp 3331–3342 | Cite as

DUK-SVD: dynamic dictionary updating for sparse representation of a long-time remote sensing image sequence

  • Lizhe Wang
  • Peng Liu
  • Weijing Song
  • Kim-Kwang Raymond Choo
Methodologies and Application

Abstract

Sparse representations of data or signals have drawn considerable attentions in the past decade. In this paper, we focus on the problem of training high-efficacy dictionaries for remote sensing images of massive long-time sequences. By extending the classical K-SVD, we propose a new dictionaries learning algorithm. Different from K-SVD, in the proposed incremental K-SVD algorithm, we selectively train a certain number of atoms when each new batch of sample data are added into the training process; current dictionary are replenished by the selected and enhanced atoms. The new atoms are initialized by information entropy. Meanwhile, we introduce an uncertainty metric to determine whether or not new atoms should be added into the current dictionary. To efficiently and sparsely represent the long-time sequence data set, we also de-correlate the dictionary based on new atoms by introducing a mutual coherence constraint into the atom updating stage. The method presented in this paper aims to adaptively and dynamically train the dictionary from big data. Two other state-of-the-art dictionary learning methods such as online dictionary learning (ODL) and recursive least squares dictionary learning algorithm (RLS-DLA) who also could train the dictionary using relatively large data, are comprehensively compared with the proposed algorithm in both sparse model and error model. In the sparse model, the reconstruction error of the DUK-SVD dictionary was smaller than ODL and RLS-DLA. In the error model, the sparsity of the DUK-SVD was higher than ODL and RLS-DLA. We can also observe that in the sparse model the proposed DUK-SVD often consume fewer computing time than ODL.

Keywords

Long-time sequence Sparse representation Dictionary learning Remote sensing 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 41571413 and 41471368).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanPeople’s Republic of China
  2. 2.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.Department of Information Systems and Cyber SecurityUniversity of Texas at San AntonioSan AntonioUSA

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