Mining multispectral aerial images for automatic detection of strategic bridge locations for disaster relief missions

Munawar, Hafiz Suliman and Zhang, Ji and Li, Hongzhou and Mo, Deqing and Chang, Liang (2019) Mining multispectral aerial images for automatic detection of strategic bridge locations for disaster relief missions. In: 8th Workshop on Biologically-Inspired Techniques for Knowledge Discovery and Data Mining (BDM 2019), held in conjunction with the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), 14-17 April 2019, Macau, China.


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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Date Deposited: 25 May 2020 23:11
Last Modified: 05 Jun 2020 05:03
Uncontrolled Keywords: image mining, disaster management, isotropic surround suppression, image processing, object recognition, linear object detection, bridge recognition, road recognition
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Identification Number or DOI: https://doi.org/10.1007/978-3-030-26142-9_17
URI: http://eprints.usq.edu.au/id/eprint/38118

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