Panchromatic Satellite Image Classification for Flood Hazard Assessment

 

Ahmed Shaker*¹, Wai Yeung Yan¹, Nagwa El-Ashmawy^1,2

 

¹ Department of Civil Engineering, Ryerson University, Toronto, Ontario, Canada. *ahmed.shaker@ryerson.ca.

² Survey Research Institute, National Water Research Center, Cairo, Egypt.

 

ABSTRACT

The study aims to investigate the use of panchromatic (PAN) satellite image data for flood hazard assessment with an aid of various digital image processing techniques. Two SPOT PAN satellite images covering part of the Nile River in Egypt were used to delineate the flood extent during the years 1997 and 1998 (before and after a high flood). Three classification techniques, including the contextual classifier, maximum likelihood classifier and minimum distance classifier, were applied to the following: 1) the original PAN image data, 2) the original PAN image data and grey-level co-occurrence matrix texture created from the PAN data, and 3) the enhanced PAN image data using an edge-sharpening filter. The classification results were assessed with reference to the results derived from manual digitization and random checkpoints. Generally, the results showed improvement of the calculation of flood area when an edge-sharpening filter was used. In addition, the maximum likelihood classifier yielded the best classification accuracy (up to 97%) compared to the other two classifiers. The research demonstrates the benefits of using PAN satellite imagery as a potential data source for flood hazard assessment.

Keywords: Panchromatic imagery, flood hazard assessment, texture analysis, image classification.

 

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