Side-scan sonar image segmentation using Kernel-based Extreme Learning Machine
Autonomous Underwater Vehicles (AUVs) are important platform for oceanographic survey. AUVs have been widely applied to many fields, such as the ocean research, oil and gas exploitation, mineral resources investigation, fishing and military. People can obtain important ocean information by segmenting, classifying and recognizing sonar image of AUV. So studying side-scan sonar image is significant. Markov Random Field (MRF) is an efficient method for segmentation of side-scan sonar image. However, MRF may not work well for side-scan sonar image obtained from complex environment. In these images, pixel values do not change obviously. In this paper, an innovative segmentation method based MRF and Kernel-based Extreme Learning Machine (K-ELM) is proposed for real side-scan sonar image segmentation. This method has been validated on the real sonar images. Experimental results demonstrate that the proposed method outperforms MRF in classification accuracy.