Skip to main content
Log in

Current progress of information fusion in China

  • Progress
  • Information Science
  • Published:
Chinese Science Bulletin

Abstract

Information fusion is a challenging task to extract the required information for high-level applications from various homogeneous or heterogeneous sensors. This paper summarizes some improvements in recent five years in the presented structure of information fusion, including estimation fusion, image fusion and others. Some technologies of information fusion and their variants are discussed. Although the state-of-the-art of the algorithms for information fusion have been proposed, there still remains some fundamental challenges with regard to exploiting the emerging multi-sensors’ characteristics and their special structures. Finally, some potential prospects of estimation fusion and image fusion are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Raol J R. Multi-sensor Data Fusion with MATLAB. Boca Raton: CRC Press, 2010

    Google Scholar 

  2. Liu B, Peng J X. Multi-spectral image fusion method based on two channels non-separable wavelets. Sci China Ser F-Inform Sci, 2008, 51: 2022–2032

    Article  MathSciNet  MATH  Google Scholar 

  3. Yin H T, Li S T. Multimodal image fusion with joint sparsity model. Opt Eng, 2011, 50: 067007

    Article  ADS  Google Scholar 

  4. Yang B, Li S T. Multifocus image fusion and restoration with sparse representation. IEEE Trans Instrum Meas, 2010, 59: 884–892

    Article  Google Scholar 

  5. Yang B, Li S T. Pixel-level image fusion with simultaneous orthogonal matching pursuit. Inform Fusion, 2012, 13: 10–19

    Article  Google Scholar 

  6. Zhu X X, Wang X, Bamler R. Compressive sensing for image fusionwith application to pan-sharpening. In: IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), 2011. 2793–2796

    Google Scholar 

  7. Wan T, Canagarajah N, Achim A. Compressive image fusion. In: Proceedings of the 15th IEEE International Conference on Image Processing, 2008. 1308–1311

    Google Scholar 

  8. Lian F, Han C Z, Liu W F, et al. Joint spatial registration and multitarget tracking using an extended PM-CPHD filter. Sci China Inform Sci, 2012, 55: 501–511

    Article  MathSciNet  Google Scholar 

  9. Wang Y, Jing Z L, Hu S Q, et al. On the sensor order in sequential integrated probability data association filter. Sci China Inform Sci, 2012, 55: 491–500

    Article  MathSciNet  MATH  Google Scholar 

  10. Ling Q, Fu Y F, Tian Z. Localized sensor management for multi-target tracking in wireless sensor networks. Inform Fusion, 2011, 12: 194–201

    Article  Google Scholar 

  11. Luo Y, Zhu Y, Shen X, et al. Novel data association algorithm based on integrated random coefficient matrices Kalman filtering. IEEE Trans Aerospace Electr Syst, 2012, 48: 144–158

    Article  ADS  Google Scholar 

  12. Li X R, Zhu Y, Wang J, et al. Optimal linear estimation fusion I: Unified fusion rules. IEEE Trans Inform Theory, 2003, 49: 2192–2208

    Article  MATH  Google Scholar 

  13. Zhu Y M. Multisensor Decision and Estimation Fusion. Boston, MA: Kluwer, 2003

    Book  Google Scholar 

  14. Bar-Shalom Y. Update with out-of-sequence measurement in tracking: Exact solution. In: Proceedings of the 2000 SPIE Conference on Signal and data processing of small targets. Bellingham, WA, USA: Society of Photo-Optical instrumentation engineers, 2000. 541–556

    Chapter  Google Scholar 

  15. Bar-Shalom Y. Update with out-of-sequence measurements in tracking: Exact solution. IEEE Trans Aerospace Electr Syst, 2002, 38: 769–777

    Article  ADS  Google Scholar 

  16. Zhang K S, Li X R, Zhu Y M. Optimal update with out-of-sequence measurements for distributed filtering. In: Proceedings of the IEEE Fifth International Conference on Information Fusion, 2002. 1519–1526

    Google Scholar 

  17. Challa S, Evans R J, Wang X. A Bayesian solution and its approximations to out-of-sequence measurement problems. Inform Fusion, 2003, 4: 185–199

    Article  Google Scholar 

  18. Xu J, Song E B, Luo Y T, et al. Optimal distributed Kalman filtering fusion algorithm without inconvertibility of estimation error and sensor noise covariances. IEEE Sign Process Lett, 2012, 19: 55–58

    Article  ADS  Google Scholar 

  19. Shen X J, Zhu Y M, He L M, et al. A near-optimal iterative algorithm via alternately optimizing sensor and fusion rules in distributed decision systems. IEEE Trans Aerospace Electr Syst, 2011, 47: 2514–2529

    Article  ADS  Google Scholar 

  20. Shen X J, Zhu Y M, Song E B, et al. Minimizing Euclidian state estimation error for linear uncertain dynamic systems based on multisensor and multi-algorithm fusion. IEEE Trans Inform Theory, 2011, 57: 7131–7146

    Article  MathSciNet  Google Scholar 

  21. Wang Y M, Li X R. Distributed estimation fusion with unavailable cross-correlation. IEEE Trans Aerospace Electr Syst, 2012, 48: 259–278

    Article  ADS  Google Scholar 

  22. Luo Y T, Shen X J, Zhu Y M. Integrated optimization methods in multisensor decision and estimation fusion. Sci China Inform Sci, 2012, 55: 545–550

    Article  MathSciNet  MATH  Google Scholar 

  23. Shen X J, Zhu Y M, You Z S. A sensor quantization algorithm for best linear estimation fusion in bandwidth-constrained systems. In: IEEE International Conference on Intelligent Control and Information Processing (ICICIP), 2010. 433–438

    Google Scholar 

  24. Shen X J, Luo Y T, Zhu YM, et al. Globally optimal distributed Kalman filtering fusion. Sci China Inform Sci, 2012, 55: 512–529

    Article  MathSciNet  MATH  Google Scholar 

  25. Shen X J, Song E B, Zhu Y M, et al. Globally optimal distributed Kalman fusion with local out-of-sequence-measurement updates. IEEE Trans Automat Control, 2009, 54: 1928–1934

    Article  MathSciNet  Google Scholar 

  26. Zhang W A, Feng G, Yu L. Multi-rate distributed fusion estimation for sensor networks with packet losses. Automatica, 2012, 48: 2016–2028

    Article  MathSciNet  MATH  Google Scholar 

  27. Shen X J, Zhu Y M, You Z S, et al. Optimal centralized update with multiple local out-of-sequence measurements. IEEE Trans Signal Process, 2009, 57: 1551–1562

    Article  ADS  MathSciNet  Google Scholar 

  28. Luo Y T, Zhu YM. Distributed Kalman filtering fusion with packet loss or intermittent communications from local estimators to fusion center. J Syst Sci Complex, 2012, 25: 463–485

    Article  MathSciNet  MATH  Google Scholar 

  29. Shen X J, Zhu Y M, You Z S. Optimal flight path update with adding or removing out-of-sequence measurements. In: IEEE International Conference on Intelligent Control and Information Processing (ICICIP), 2010. 427–432

    Google Scholar 

  30. Xu J, Li J X, Xu S. Data fusion for target tracking in wireless sensor networks using quantized innovations and Kalman filtering. Sci China Inform Sci, 2012, 55: 530–544

    Article  MathSciNet  MATH  Google Scholar 

  31. Ma J, Sun S L. Information fusion estimators for systems with multiple sensors of different packet dropout rates. Inform Fusion, 2011, 12: 213–222

    Article  Google Scholar 

  32. Liu H P, Sun F C. Fusion tracking in color and infrared images using joint sparse representation. Sci China Inform Sci, 2012, 55: 590–599

    Article  MathSciNet  Google Scholar 

  33. Wang X, Liu L, Tang ZM. Infrared human tracking with improved mean shift algorithm based on multicue fusion. Appl Opt, 2009, 48: 4201–4212

    Article  ADS  PubMed  Google Scholar 

  34. Zhang C L, Jing Z L, Jin B, et al. A dual-kernel-based tracking approach for visual target. Sci China Inform Sci, 2012, 55: 566–576

    Article  MathSciNet  Google Scholar 

  35. Liu H P, Sun F C, Yu L P, et al. Vehicle tracking using stochastic fusionbased particle filter. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007. 2735–2740

    Google Scholar 

  36. Liu H P, Sun F C. Fusion tracking in color and infrared images using sequential belief propagation. In: IEEE International Conference on Robotics and Automation, 2008. 2259–2264

    Google Scholar 

  37. Sun Q S, Zeng S G, Liu Y, et al. A new method of feature fusion and its application in image recognition. Pattern Recogn, 2005, 38: 2437–2448

    Article  Google Scholar 

  38. Zhao J, Fan Y Y, Fan W T. Fusion of global and local feature using KCCA for automatic target recognition. In: Fifth IEEE International Conference on Image and Graphics, 2009. 958–962

    Google Scholar 

  39. Fu Y, Cao L L, Guo G D, et al. Multiple feature fusion by subspace learning. In: Proceedings of the 2008 international conference on Content-based image and video retrieval. ACM, 2008. 127–134

    Chapter  Google Scholar 

  40. Zhai D M, Chang H, Shan S G, et al. Multiview metric learning with global consistency and local smoothness. ACM Trans Intel Syst Tech, 2012, 3: 53

    Article  Google Scholar 

  41. Davenport M A, Hegde C, Duarte M F, et al. Joint manifolds for data fusion. IEEE Trans Image Process, 2010, 19: 2580–2594

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  42. Zhou Z H. Unlabeled data and multiple views. In: Partially Supervised Learning. Berlin, Heidelberg: Springer, 2012. 1–7

    Chapter  Google Scholar 

  43. Das S. High-level Data Fusion. Norwood, MA: Artech House, 2008

    Google Scholar 

  44. Zhou Z H. Ensemble Methods: Foundations and Algorithms. Boca Raton: CRC Press, 2012

    Google Scholar 

  45. Han D Q, Deng Y, Han C Z, et al. Some notes on betting commitment distance in evidence theory. Sci China Inform Sci, 2012, 55: 558–565

    Article  MathSciNet  Google Scholar 

  46. Han D Q, Dezert J, Han C Z, et al. New dissimilarity measures in evidence theory. In: Proceedings of the 14th IEEE International Conference on Information Fusion (FUSION) 2011. 1–7

    Google Scholar 

  47. Wen C L, Xu X B, Jiang H N, et al. A new DSmT combination rule in open frame of discernment and its application. Sci China Inform Sci, 2012, 55: 551–557

    Article  Google Scholar 

  48. Zhao Y Q, Zhang G H, Jie F R, et al. Unsupervised classification of spectropolarimetric data by region-based evidence fusion. IEEE Geosci Remote Sens Lett, 2011, 8: 755–759

    Article  ADS  Google Scholar 

  49. Song E B, Shen X J, Zhou J, et al. Performance analysis of communication direction for two-sensor tandem binary decision system. IEEE Trans Inform Theory, 2009, 55: 4777–4785

    Article  MathSciNet  Google Scholar 

  50. Chen X W, Li Q, Li X, et al. Video motion stitching using trajectory and position similarities. Sci China Inform Sci, 2012, 55: 600–614

    Article  MathSciNet  Google Scholar 

  51. Cao L, Chen X Q, Sheng T. An algorithm for high precision attitude determination when using low precision sensors. Sci China Inform Sci, 2012, 55: 626–637

    Article  MathSciNet  Google Scholar 

  52. Jiang Y, Chen J, Wang R S. Fusing local and global information for scene classification. Opt Eng, 2010, 49: 047001

    Article  ADS  Google Scholar 

  53. Zhang T H, Li X L, Tao D C, et al. Multimodal biometrics using geometry preserving projections. Pattern Recogn, 2008, 41: 805–813

    Article  MATH  Google Scholar 

  54. Zhang R F, Zhang Z F, Li M J, et al. A probabilistic semantic model for image annotation and multimodal image retrieval. In: Tenth IEEE International Conference on Computer Vision, 2005. 846–851

    Google Scholar 

  55. Cam T N, Zhan D C, Zhou Z H. Multi-modal image annotation with multi-instance multi-label LDA. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI’13), 2013

    Google Scholar 

  56. Mitchell H B. Image Fusion: Theories, Techniques and Applications. Berlin: Springer, 2010

    Book  Google Scholar 

  57. Luo X Y, Zhang J, Dai Q H. Saliency based geometry measurement for image fusion performance. IEEE Trans Instrum Meas, 2012, 61: 1130–1132

    Article  Google Scholar 

  58. Zhang Q, Wang L, Li H J, et al. Video fusion performance evaluation based on structural similarity and human visual perception. Signal Process, 2012, 92: 912–925

    Article  Google Scholar 

  59. Han Y, Cai Y Z, Cao Y, et al. A new image fusion performance metric based on visual information fidelity. Inform Fusion, 2013, 14: 127–135

    Article  Google Scholar 

  60. Jing Z L, Xiao G, Li Z H. Image Fusion: Theory and Applications. Beijing: Higher Education Press, 2007

    Google Scholar 

  61. Zhang X Q, Liu C Y, Men T, et al. Infrared and visible image fusion using NSCT and GGD. In: 3rd International Conference on Digital Image Processing, 2011. 80090V

    Google Scholar 

  62. Kong W, Lei Y, Ni X. Fusion technique for grey-scale visible light and infrared images based on non-subsampled contourlet transform and intensity-hue-saturation transform. IET Sign Process, 2011, 5: 75–80

    Article  Google Scholar 

  63. Wang H J, Yang Q K, Li R. Tunable-Q contourlet-based multi-sensor image fusion. Signal Process, 2013, 93: 1879–1891

    Article  Google Scholar 

  64. Shi C, Miao Q G, Xu P F. A novel algorithm of image fusion based on shearlets and PCNN. Neurocomputing, 2013, 117: 47–53

    Article  Google Scholar 

  65. Wang L, Li B, Tian L F. Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shiftinvariant shearlet coefficients. Inform Fusion, 2012, http://dx.doi.org/10.1016/j.inffus.2012.03.002

    Google Scholar 

  66. Hu J W, Li S T. The multiscale directional bilateral filter and its application to multisensor image fusion. Inform Fusion, 2012, 13: 196–206

    Article  Google Scholar 

  67. Yin H T, Li S T, Fang L Y. Simultaneous image fusion and superresolution using sparse representation. Inform Fusion, 2013, 14: 229–240

    Article  Google Scholar 

  68. Zhou Z M, Li Y X, Shi H Q, et al. Pan-sharpening: A fast variational fusion approach. Sci China Inform Sci, 2012, 55: 615–625

    Article  MathSciNet  Google Scholar 

  69. Niu Y F, Xu S T, Wu L Z, et al. Airborne infrared and visible image fusion for target perception based on target region segmentation and discrete wavelet transform. Math Problems Eng, 2012, 2012: 1–10

    Google Scholar 

  70. Liu K, Guo L, Chen J S. Contourlet transform for image fusion using cycle spinning. J Syst Eng Electr, 2011, 22: 353–357

    Article  Google Scholar 

  71. Gong M G, Zhou Z Q, Ma J J. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process, 2012, 21: 2141–2151

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  72. Ma J J, Gong M G, Zhou Z Q. Wavelet fusion on ratio images for change detection in SAR images. IEEE Geosci Remote Sens Lett, 2012, 9: 1122–1126

    Article  ADS  Google Scholar 

  73. Zhang Q, Wang L, Li H J, et al. Similarity-based multimodality image fusion with shiftable complex directional pyramid. Pattern Recogn Lett, 2011, 32: 1544–1553

    Article  Google Scholar 

  74. Zhang L P, Shen H F, Gong W, et al. Adjustable model-based fusion method for multispectral and panchromatic images. IEEE Trans Syst Man Cybern, 2012, 42: 1693–1704

    Article  Google Scholar 

  75. Pan H, Xiao G, Jing Z L. Feature-based image fusion scheme for satellite recognition. In: IEEE 13th Conference on Information Fusion, 2010. 1–6

    Google Scholar 

  76. Chen N, Sun F C, Ding L G, et al. An adaptive PNN-DS approach to classification using multi-sensor information fusion. Neural Comput Appl, 2009, 18: 455–467

    Article  Google Scholar 

  77. Yin P P, Sun F C, Wang C, et al. An adaptive feature fusion framework for multi-class classification based on SVM. Soft Comput, 2008, 12: 685–691

    Article  Google Scholar 

  78. Luo B, Khan M M, Bienvenu T, et al. Decision-based fusion for pansharpening of remote sensing images. IEEE Geosci Remote Sens Lett, 2013, 10: 19–23

    Article  ADS  CAS  Google Scholar 

  79. Pan H, Jing Z L, Liu R L, et al. Simultaneous spatial-temporal image fusion using Kalman filtered compressed sensing. Opt Eng, 2012, 51: 057005

    Article  ADS  Google Scholar 

  80. Song H H, Huang B. Spatiotemporal satellite image fusion through onepair image learning. IEEE Trans Geosci Remote Sens, 2013, 51: 1883–1896

    Article  ADS  Google Scholar 

  81. Zhang Q, Wang L, Ma Z K, et al. A novel video fusion framework using surfacelet transform. Opt Commun, 2012, 285: 3032–3041

    Article  ADS  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Han Pan.

Additional information

This article is published with open access at Springerlink.com

About this article

Cite this article

Jing, Z., Pan, H. & Qin, Y. Current progress of information fusion in China. Chin. Sci. Bull. 58, 4533–4540 (2013). https://doi.org/10.1007/s11434-013-6092-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11434-013-6092-8

Keywords

Navigation