The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVI-4/W3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W3-2021, 257–260, 2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W3-2021, 257–260, 2022
11 Jan 2022
11 Jan 2022


N. A. Muhadi1, A. F. Abdullah1,2, S. K. Bejo1, M. R. Mahadi1, and A. Mijic3 N. A. Muhadi et al.
  • 1Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • 2Institute of Aquaculture and Aquatic Sciences, Batu 7, Jalan Kemang 6, Teluk Kemang, 71050 Si Rusa, Port Dickson, Negeri Sembilan, Malaysia
  • 3Department of Civil and Environmental Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK

Keywords: Hybrid technique, Image segmentation, Water level fluctuation, Water segmentation, Surveillance camera

Abstract. Floods are the most frequent type of natural disaster that cause loss of life and damages to personal property and eventually affect the economic state of the country. Researchers around the world have been made significant efforts in dealing with the flood issue. Computer vision is one of the common approaches being employed which include the use of image segmentation techniques for image understanding and image analysis. The technique has been used in various fields including in flood disaster applications. This paper explores the use of a hybrid segmentation technique in detecting water regions from surveillance images and introduces a flood index calculation to study water level fluctuations. The flood index was evaluated by comparing the result with water level measured by sensor on-site. The experimental results demonstrated that the flood index reflects the trend of water levels of the river. Thus, the proposed technique can be used in detecting water regions and monitoring the water level fluctuation of the river.