Optimized Rule-Based Flood Mapping Technique Using Multitemporal RADARSAT-2 Images in the Tropical Region

Abstract

Flood is one of the most common natural disasters in Malaysia. Preparing an accurate flood inventory map is the basic step in flood risk management. Flood detection is a complex process because of the limitation of methodological approaches and cloud coverage over tropical areas. An efficient approach is proposed to identify flooded areas using multitemporal RADARSAT-2 imageries. First, multispectral Landsat image was used to extract and subtract permanent water bodies, and this image was later utilized to extract the same information from multitemporal RADARSAT-2 imageries. Next, water bodies during a flood event were extracted from RADARSAT-2 images. Permanent water bodies, shadow, and paddy were detected from synthetic aperture radar (SAR) images by analyzing their temporal backscattering values. During feature extraction, rule-based object-oriented technique was applied to classify both SAR and Landsat images. Image segmentation during object-based analysis was performed to distinguish the boundaries of various dimensions and scales of objects. Moreover, a Taguchi-based method was employed to optimize the segmentation parameters. After segmentation, the rules were defined and images were classified to produce an accurate flood inventory map for the 2014 Kelantan flood. A confusion matrix was generated to evaluate the performance of the classification method. The overall accuracy of 86.16% was achieved for RADARSAT-2 using rule-based classification and optimization technique. The resulting flood inventory map using the proposed approach supported the efficiency of the proposed methodology.