Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence

This paper investigates a new approach for flood evaluation based on multi-sensor satellite images utilizing swarm intelligence techniques. The swarm intelligence techniques used are Genetic Algorithm (GA) for image registration and Niche Particle Swarm Optimization (NPSO) for image clustering. Analysis of satellite images are applied in two stages: Linear Imaging Self Scanning Sensor (LISS-III) image acquired before-flood and Synthetic Aperture Radar (SAR) image acquired during-flood. In the first step, SAR image is aligned with LISS-III image using GA. The aligned SAR image (during-flood) is used to extract flooded and non-flooded regions where as LISS-III image (before-flood) is used to classify various land cover regions. For this image clustering is carried out where cluster centers are generated using the cluster splitting technique such as NPSO. The data points are grouped into their respective classes using the merging method. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. The performance comparisons of these swarm intelligence techniques with conventional methods are presented.