Comparison between Pixel-based and Object-based Classifications Using Radar Satellite Image in Extracting Massive Flood Extent at Northern Region of Peninsular Malaysia

Year 2010 massive flood hit the northern region of Peninsular Malaysia particularly Perlis and Kedah involved several districts and destroyed many agricultural areas and the infrastructure. This study focuses on the comparison between pixel-based classification and object-based classification of five machine learning algorithms including Parallelepiped (PP), Minimum Distance (MD), Maximum Likelihood (ML), Mahalanobis Distance (MH) and Neural Network (NN) using radar satellite image in extracting that flood extent. TerraSAR-X image was used to map the flood extent of the study area. In object-based approach, there were three simple machine learning algorithms such as PP, MD, MH together with NN performed with high accuracy while in pixel based approach, NN was the highest accuracy of all machine learning algorithms. The best output was chosen to be converted to vector format for mapping the flood extent. The result showed clearly through the map output that Kubang Pasu, Kota Setar and Kangar districts were highly affected by the flood. From the flood extent information, the collaboration of government, private sector, Non Governmental Organization (NGO) and community are needed to play the appropriate role in managing flood damage especially at the highly affected area and thus prevent loss of human live. Besides that, the authority could take action plan for pre-disaster, during and post-disaster caused by flooding.