OSA's Digital Library

Optics Letters

Optics Letters


  • Editor: Alan E. Willner
  • Vol. 38, Iss. 23 — Dec. 1, 2013
  • pp: 5146–5149

Unsupervised change detection of satellite images using low rank matrix completion

Shibo Gao, Yongmei Cheng, and Yongqiang Zhao  »View Author Affiliations

Optics Letters, Vol. 38, Issue 23, pp. 5146-5149 (2013)

View Full Text Article

Enhanced HTML    Acrobat PDF (405 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Traditional unsupervised change detection methods need to generate a difference image (DI) for subsequent processing to produce a binary change map. In addition, few methods explore global structures. This Letter presents a novel unsupervised change detection approach based on low rank matrix completion. Other than generating a DI, the changed pixels are modeled as the estimated missing values for matrix completion, where the changed pixels are represented by a sparse term. A common low rank matrix is recovered by two temporal images. The changed pixels are separated out from the low rank matrix, in which the local information is introduced via graph cuts. The global and local structures are utilized in our model. Experimental results validate the effectiveness of the proposed approach. The proposed method is a new view for change detection.

© 2013 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(330.1880) Vision, color, and visual optics : Detection

ToC Category:
Vision, Color, and Visual Optics

Original Manuscript: July 8, 2013
Manuscript Accepted: September 23, 2013
Published: November 26, 2013

Shibo Gao, Yongmei Cheng, and Yongqiang Zhao, "Unsupervised change detection of satellite images using low rank matrix completion," Opt. Lett. 38, 5146-5149 (2013)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. Z. Yetgin, IEEE Trans. Geosci. Remote Sens. 50, 1919 (2012). [CrossRef]
  2. M. Gong, Z. Zhou, and J. Ma, IEEE Trans. Image Process. 21, 2141 (2012). [CrossRef]
  3. T. Celik, IEEE Trans. Geosci. Remote Sens. 6, 772 (2009). [CrossRef]
  4. S. Patra, S. Ghosh, and A. Ghosh, Int. J. Remote Sens. 32, 6071 (2011). [CrossRef]
  5. N. S. Mishra, S. Ghosh, and A. Ghosh, Appl. Soft Comput. 12, 2683 (2012). [CrossRef]
  6. E. J. Candès and B. Recht, Found. Comput. Math. 9, 717 (2009). [CrossRef]
  7. J. Liu, P. Musialski, P. Wonka, and J. Ye, IEEE Trans. Pattern Anal. Mach. Intell. 35, 208 (2013). [CrossRef]
  8. L. Wu, A. Ganesh, B. Shi, Y. Matsushita, Y. Wang, and Y. Ma, in Proceedings of Asian Conference on Computer Vision (2011), p. 703.
  9. V. Kolmogorov and R. Zabin, IEEE Trans. Pattern Anal. Mach. Intell. 26, 147 (2004). [CrossRef]
  10. Z. Lin, M. Chen, and Y. Ma, “The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices,” arXiv:10095055 (2010).
  11. G. Ye, D. Liu, I.-H. Jhuo, and S.-F. Chang, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), p. 3021.
  12. K.-C. Toh and S. Yun, Pacific J. Optim. 6, 615 (2010).
  13. E. J. Candès, X. Li, Y. Ma, and J. Wright, J. ACM 58, 1 (2011). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited