PCA and Kernel-based extreme learning machine for side-scan sonar image classification

As an important role of oceanographic survey, side-scan sonar image classification has attracted much attention in the past two decades. Due to the special properties of sonar image, traditional approaches are difficult to get good classification accuracy, so their implementation in real world is blocked. In this paper, a novel classification system based on kernel-based extreme learning machine (KELM) and principle component analysis (PCA) is proposed. Experimental results demonstrate that the proposed method can get better stability and higher classification accuracy than traditional approaches such as support vector machine (SVM).