Segmentation of underwater acoustic images by FCM with EMD

Abstract

In recent years, there is an expanding enthusiasm for studying the seabed structure. The investigation of seabed has numerous applications, for example, protest recognition, exploration of natural resources, angling, analyzing the composition of residues, etc. In oceanic examinations, submerged objects location is a standout amongst the most basic errands. This emphasizes the necessity of image segmentation, which divides an image into parts that have strong correlations with objects to reflect the actual information collected from the real world. Image segmentation is the most practical approach to virtually all automated image recognition systems. Clustering of numerical data forms the basis of many classification and system modelling algorithms. The purpose of clustering is to identify natural groupings of data from a large data set to produce a concise representation of a system's behaviour. In this paper, Empirical Mode Decomposition (EMD) picture upgrade procedure has been utilized to enhance the nature of sonar pictures and Fuzzy C means (FCM) clustering method is used for underwater image segmentation. The result of EMD is compared with histogram equalization and Contrast stretching. EMD with K-means and EMD with FCM is compared and the exploratory outcomes demonstrate that the proposed technique can acquire exact outcomes and enhances operational productivity.