Abstract
We propose in this paper an image mining technique based on multispectral aerial images for automatic detection of strategic bridge locations for disaster relief missions. Bridge detection from aerial images is a key landmark that has vital importance in disaster management and relief missions. UAVs have been increasingly used in recent years for various relief missions during the natural disasters such as floods and earthquakes and a huge amount of multispectral aerial images are generated by UAVs in the missions. Being a multi- stage technique, our method utilizes these multispectral aerial images for identifying patterns for effective mining of bridge locations. Experimental results on real-world and synthetic images are conducted to demonstrate the effectiveness of our proposed method, showing that it is 40% faster than the existing Automatic Target Recognition (ATR) systems and can achieve a 95% accuracy. Our technique is believed to be able to help accelerate and enhance the effectiveness of the relief missions carried out during disasters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Memon, N.: Malevolent Floods of Pakistan, 2010–2012 (2013)
Khan, S.I., Hong, Y., Gourley, J.J., Khattak, M.U., De Groeve, T.: Multi-sensor imaging and space-ground cross-validation for 2010 flood along Indus River, Pakistan. Remote Sens. 6(3), 2393–2407 (2014)
Zhen, Z., Tianxu, Z., Guoyou, W.: Research on bridge recognition in long-range infrared images. ACTA Electronica Sinica 26(11) (1998)
Zaihua, Y., Shuqian, Y.: Automatic recognition and tracking techniques for infrared bridge image. Infrared Laser Eng. (1998)
Hsu, W., Lee, M.L., Zhang, J.: Image mining: trends and developments. J. Intell. Inf. Syst. 19(1), 7–23 (2002)
Gedika, E., Çinara, U., Karamana, E., Yardımcıa, Y., Halıcıb, U., Pakinc, K.: A new robust method for bridge detection from high resolution electro-optic satellite images. In: Proceedings of IEEE International GeoSciene and Remote Sensing Symposium, vol. 6 (2003)
Sui, H., Gong, J., Xiao, J., Li, M.: Automatic recognition of bridges over water and registration in remotely sensed images with GIS data. In: Proceedings of the ISPRS Commission VII Mid-term Symp, vol. 36 (2006)
Li, M., Yandong, T., Zelin, S.: Segmentation and recognition of bridge over water based on Mumford-Shah model. Infrared Laser Eng. 35(4), 499 (2006)
Abraham, L., Sasikumar, M.: A fully automatic bridge extraction technique for satellite images. Int. J. Inf. Process. 6(3), 89–97 (2012)
Chaudhuri, D., Samal, A.: An automatic bridge detection technique for multispectral images. In: CSE Conference and Workshop Papers (2008)
Fu, Y., Xing, K., Huang, Y., Xiao, Y.: Recognition of bridge over water in high-resolution remote sensing images. In: 2009 WRI World Congress on Computer Science and Information Engineering (2009)
Beuliga, S., von Schonermarka, M., Hubera, F.: A FPGA-based automatic bridge over water recognition in high-resolution satellite images. In: Image and Signal Processing for Remote Sensing XVIII, Proceedings of SPIE, vol. 8537 (2012)
Soyman, Y.: Robust automatic target recognition in FLIR imagery. In: Proceedings of SPIE, vol. 8391 (2012)
Trias-Sanz, R., Loménie, N.: Automatic bridge detection in high-resolution satellite images. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds.) ICVS 2003. LNCS, vol. 2626, pp. 172–181. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36592-3_17
Cao, Z., Zhang, X.: Forward-looking infrared target recognition based on histograms of oriented gradients. In: Proceedings of SPIE, vol. 8003 (2011)
Yuan, X.-H., Jin, L.-Z., Li, J.-X., Xia, L.-Z.: Recognition of bridges over water through detecting and analyzing regions of interest. J. Infrared Millimeter Waves 22(5), 331–336 (2003)
Xu, W., Xu, L., Hu, Y.: Adaptive edge detection using a half neighborhood algorithm. J. Shanghai Univ. (Nat. Sci.) 12(2), 146–149 (2006)
Ji, Z., Hsu, W., Lee, M.L.: Image mining: issues, frameworks and techniques. In: Proceedings of the Second International Conference on Multimedia Data Mining. Springer, Heidelberg (2001)
Zahradnikova, B., Duchovicova, S., Schreiber, P.: Image mining: review and new challenges. Int. J. Adv. Comput. Sci. Appl. 6(7), 242–246 (2015)
Zhang, J.: Advancements of outlier detection: a survey. ICST Trans. Scalable Inf. Syst. 13(1), 1–26 (2013)
Zhao, C., Shi, W., Deng, Y.: Novel edge detection method based on gradient. J. Opto-Electron. Eng. (2005)
Acknowledgement
The authors would like to thank the support from Guangxi Key Laboratory of Trusted Software (No. kx201615), Capacity Building Project for Young University Staff in Guangxi Province, Department of Education of Guangxi Province (No. ky2016YB149) and Major research project, Bureau of Science and Technology of Guilin (No.: 20180101-3).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Munawar, H.S., Zhang, J., Li, H., Mo, D., Chang, L. (2019). Mining Multispectral Aerial Images for Automatic Detection of Strategic Bridge Locations for Disaster Relief Missions. In: U., L., Lauw, H. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11607. Springer, Cham. https://doi.org/10.1007/978-3-030-26142-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-26142-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26141-2
Online ISBN: 978-3-030-26142-9
eBook Packages: Computer ScienceComputer Science (R0)