Fusion of SAR Images for Improved Classification of Flooded Areas in the Northern Peninsular Malaysia

The northern peninsular of Malaysia has lost a lot of lives and property worth billions to the series of floods that have been occurring for many years. Many disaster management strategies have been adopted by the Malaysian government in handling these flood disasters but it is still a topic in the annual agenda. This research project aimed at using fusion techniques in order to obtain more interpretable information and boundaries in classification of SAR images which can aid in addressing the flood disastrous challenges in the study area. This was achieved through the fusion of two Radarsat-1 images and a TerraSAR-X image of the study area and secondly by investigation of a suitable fusion technique for delineation of flooded areas in the study area. The techniques used include the Hue Saturation and Value (HSV), the Brovey Transformation (BT), the Gram Schmidt (GS) and the Principal Component Spectral Sharpening (PCSS), which were classified using Maximum Likelihood (ML) and support Vector Machine (SVM). The results indicated that BT is the most suitable among all the fusion techniques used having the highest overall accuracy of 70.9615% and kappa coefficient of 0.3418.