Cognitive Neurodynamics

, Volume 7, Issue 2, pp 143–154 | Cite as

Airport detection in remote sensing images: a method based on saliency map

Research Article
First Online:
Received:
Revised:
Accepted:

Abstract

The detection of airport attracts lots of attention and becomes a hot topic recently because of its applications and importance in military and civil aviation fields. However, the complicated background around airports brings much difficulty into the detection. This paper presents a new method for airport detection in remote sensing images. Distinct from other methods which analyze images pixel by pixel, we introduce visual attention mechanism into detection of airport and improve the efficiency of detection greatly. Firstly, Hough transform is used to judge whether an airport exists in an image. Then an improved graph-based visual saliency model is applied to compute the saliency map and extract regions of interest (ROIs). The airport target is finally detected according to the scale-invariant feature transform features which are extracted from each ROI and classified by hierarchical discriminant regression tree. Experimental results show that the proposed method is faster and more accurate than existing methods, and has lower false alarm rate and better anti-noise performance simultaneously.

Keywords

Visual attention Saliency map Airport detection Scale-invariant feature transform (SIFT) Hierarchical discriminant regression (HDR) tree Hough transform 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61071134) and the Research Fund for Doctoral Program of Higher Education of China (Grant No. 20110071110018).

References

  1. Bian P, Zhang L (2010) Visual saliency: a biologically plausible contourlet-like frequency domain approach. Cogn Neurodyn 4(3):189–198PubMedCrossRefGoogle Scholar
  2. Bruce ND, Tsotsos JK (2005) Saliency based on information maximization. In: Proceedings of NIPSGoogle Scholar
  3. Bruce ND, Tsotsos JK (2009) Saliency, attention, and visual search: an information theoretic approach. J Vis 9(3):1–24PubMedCrossRefGoogle Scholar
  4. Crick F, Koch C (1998) Consciousness and neuroscience. Cereb Cortex 8:97–107Google Scholar
  5. Crick F, Koch C (2003) A framework for consciousness. Nature Neurosci 119–126Google Scholar
  6. Crick F, Koch C, Kreiman G, Fried I (2004) Consciousness and neurosurgery. Neurosurgery 55:273–282PubMedCrossRefGoogle Scholar
  7. Desimone R, Duncan J (1995) Neural mechanisms of selective visual attention. Annu Rev Neurosci 18:193–222PubMedCrossRefGoogle Scholar
  8. Ding Z, Wang B, Zhang L (2011) An approach for visual attention based on biquaternion and its application for ship detection in multispectral imagery. Neurocomputing 76(1):9–17CrossRefGoogle Scholar
  9. Duda RO, Hart PE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15CrossRefGoogle Scholar
  10. Gao D, Mahadevan V, Vasconcelos N (2007) The discriminant center-surround hypothesis for bottom-up saliency. In Proceedings of NIPSGoogle Scholar
  11. Gu Y, Liljenstrom H (2007) A neural network model of attention modulated neurodynamics. Cogn Neurodyn 1(4):275–285PubMedCrossRefGoogle Scholar
  12. Guo C, Ma Q, Zhang L (2008) Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1–8Google Scholar
  13. Haab L, Trenado C, Mariam M, Strauss DJ (2011) Neurofunctional model of large-scale correlates of selective attention governed by stimulus-novelty. Cogn Neurodyn 5(1):103–111PubMedCrossRefGoogle Scholar
  14. Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Proceedings of advances in neural information processing systems, pp 545–552Google Scholar
  15. Health MT (2002) Scientific computing: an introduction survey. McGraw-Hill Press, New YorkGoogle Scholar
  16. Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1–8Google Scholar
  17. Hwang WS, Weng JY (2000) Hierarchical discriminant regression. IEEE Trans Pattern Anal Mach Intell 22(11):1277–1293CrossRefGoogle Scholar
  18. Itti L (2000) Models of bottom-up and top-down visual attention. PhD dissertation, California Inst. Chnol., PasadenaGoogle Scholar
  19. Itti L, Baldi P (2005) A principled approach to detecting surprising events in video. In: Proceedings of CVPR, pp 631–637Google Scholar
  20. Itti L, Koch C (2000) A saliency-based search mechanism for overt and covert shifts of visual attention. Vis Res 40:1489–1506PubMedCrossRefGoogle Scholar
  21. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259CrossRefGoogle Scholar
  22. Khotanzad A, Hong YH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497CrossRefGoogle Scholar
  23. Liu D, He L, Carin L (2004) Airport detection in large aerial optical imagery. In: Proceedings of IEEE international conference on acoutics, speech and signal processing, vol 5, pp 17–21Google Scholar
  24. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  25. Pi Y, Fan L, Yang X (2003) Airport detection and runway recognition in SAR images. In: Proceedings of IEEE international geoscience and remote sensing symposium, pp 4007–4009Google Scholar
  26. Qu Y, Li C, Zheng N (2005) Airport detection based on support vector machine from a single image. In: Proceedings of fifth international conference on information, communications and signal processing, pp 546–549Google Scholar
  27. Second J, Zakhor A (2007) Tree detection in urban regions using aerial lidar and image data. IEEE Geosci. Remote Sens Lett 4(2):196–200CrossRefGoogle Scholar
  28. Tao C, Tan Y, Cai H, Tian J (2011) Airport detection from large IKONOS images using clustered SIFT keypoints and region information. IEEE Geosci Remote Sens Lett 8(1):128–132CrossRefGoogle Scholar
  29. Treisman A, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12(1):97–136PubMedCrossRefGoogle Scholar
  30. Walther D, Koch C (2006) Modeling attention to salient proto-objects. Neural Netw 19(9):1395–1407PubMedCrossRefGoogle Scholar
  31. Walther D, Itti L, Riesenhuber M, Poggio T, Koch C (2002) Attentional selection for object recognition—a gentle way. Lect Notes Comput Sci 2525(1):472–479CrossRefGoogle Scholar
  32. Wang W, Li L, Hu C, Jiang Y, Kuang G (2011) Airport detection in SAR image based on perceptual organization. In: Proceedings of M2RSM, pp 1–5Google Scholar
  33. Yu Y, Wang B, Zhang L (2011) Bottom–up attention: pulsed PCA transform and pulsed cosine transform. Cogn Neurodyn 5(4):321–332PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Department of Electronic EngineeringFudan UniversityShanghaiChina
  2. 2.The Key Laboratory of Wave Scattering and Remote Sensing InformationFudan UniversityShanghaiChina

Personalised recommendations