COMPARING K- MEAN CLUSTER AND ACTIVE CONTOUR FOR OIL SPILL DETECTION

Abstract

This work aimed to compare k- mean clustering and active contour techniques for oil spill detection and
identification using ASAR images in Gulf of Mexico. This comparing of algorithms helps us to find out tracking of oil spill,
oil spill area, dark patches and spill patterns in radar images which help in regular monitoring of oil spill coverage area. As
we know K mean clustering is a vector quantization method used for oil spill detection. Here each element is partition into k
clusters which belongs to nearest mean, act as prototypes for the cluster. It works on dividing data cell into voronoi cells. K
mean cluster determine comparable spatial extent clusters. It classifies data which is new into existing clusters which called
as centroid nearest classifier. After analysing both algorithms it results that k mean clustering is more suitable for detection
of oil spill with less time duration using radar images than active contour