Multi-objective Image Segmentation using SMS-EMOA for Side Scan Sonar Data Analysis

Accurate analysis of Side Scan Sonar (SSS) images has potential applications in the identification of submerged objects like ships, planes etc. The underwater SSS images are inherently contaminated with noise. In this manuscript, a bilateral filtering algorithm is applied for noise reduction. After the noise reduction, the objective is to segment the image and determine the object, shadow, and background. The image segmentation is dealt here as a multi-objective clustering problem. Minimization of intra-cluster distance and negative of inter-cluster distance are taken as the two objective functions for analysis. The optimization task is carried out by four benchmark multi-objective optimization algorithms SMS-EMOA, RVEA, MOEA/D, and NSGA-II. Simulation study on four KLSG SSS Images reveals that the performance obtained by the SMS-EMOA algorithm is superior compared to its counterpart algorithms.