Optimized Level Set Method for Segmentation of SAR Images using Adaptive Fuzzy K Means Clustering

Abstract

The complications that occurred in remote sensing image information and analysis algorithms growth of a large scale image segmentation haven’t kept a place with the requirement for the methods which to develop the final accuracy of object detection as well as the recognition. Traditional Level set segmentation methods which are Chan-Vese (CV), Image and Vision Computing (IVC) 2010, ACM with SBGFRLS, and Online Region-Based ACM (ORACM) are suffered from more amounts of time complexity, as well as low segmentation accuracy due to large intensity homogeneities and the noise at which the region based segmentation is impossible. So this is the reason, we proposed a navel hybrid methodology called adaptive particle swarm optimization (PSO) based Fuzzy K-Means clustering algorithm. The proposed approach is diversified into two stages; in stage one, pre-processing the input image to improve the clustering efficiency and overcome the obstacles present in traditional methods by using particle swarm optimization (PSO) and Adaptive Fuzzy K-means clustering algorithm. With the help of the PSO algorithm, we getithe “optimum” pixels values are extracted from the input SAR images, these optimum values are automatically acted as clusters centers for Adaptive Fuzzy K-Means Clustering instead of random initialization from the original image. The pre-processing segmentation result improved the clustering efficiency but suffers from few drawbacks such as boundary leakages and outliers even particle Swarm optimization is used. To overcome the above drawbacks post-processing is needed to facilitate the superior segmentation results with the help of the level set method. It utilizes an efficient curve deformation driven by external and internal forces to capture the important structures (usual iedges) in an image. The combined approach of both pre-processing and post-processingwhich is called Particle Swarm Optimization based Adaptive Fuzzy-K-Means (AFKM) clustering via the level set method. The proposed approach is successfully implemented on large