Abstract
As a Widely Used Segmentation Scheme, Markov Random Field (Mrf) Utilizes K-Means Clustering to Calculate the Initial Model for Sidescan Sonar Image Segmentation. However, for the Noise and Intensity Inhomogeneity Nature of the Sidescan Sonar Images, the Segmentation Results of K-Means Clustering Have Low Accuracy, Motivating Us to Use Machine Learning Methods to Initialize Mrf. Meanwhile, an Extreme Learning Machine (Elm), a Supervised Learning Algorithm Derived from the Single-Hidden-Layer Feedforward Neural Networks, Learns Faster Than Randomly Generated Hidden-Layer Parameters and is Superior to a Support Vector Machine (Svm). Therefore, in This Paper, We Proposed a Novel Method for Sidescan Sonar Image Segmentation based on Mrf and Elm. the Proposed Method Segments Sidescan Sonar Images in Object-Highlight, Object-Shadow, and Sea-Bottom Reverberation Areas. Specifically, We Intend to Use an Elm to Get an Initial Model for Mrf. Moreover, to Improve the Stability of an Elm, a Simple Ensemble Elm (Se-Elm) based on an Ensemble Algorithm is Utilized to Obtain the Prediction Model. in a Se-Elm, We Use an Ensemble of Elms and Majority Votes to Determine the Prediction of Testing Data Sets. Then, the Classification Results of the Se-Elm Are Utilized to Initialize Mrf, Termed as Se-Elm-Mrf. with Features Consisting of Pixels of Small Image Patches, Our Experiments on Real Sonar Data Indicate that the Se-Elm Performs Better Than Other Machine Learning Methods Such as Elm, Kernel-Based Extreme Learning Machine, Svm, and Convolutional Neural Networks. Moreover, using Se-Elm as the Initial Method in the Proposed Se-Elm-Mrf, the Segmentation Results Are Smoother and the Segmentation Process Converges Faster Than the Traditional Mrf.
Recommended Citation
Y. Song et al., "Segmentation of Sidescan Sonar Imagery using Markov Random Fields and Extreme Learning Machine," IEEE Journal of Oceanic Engineering, vol. 44, no. 2, pp. 502 - 513, article no. 8361070, Institute of Electrical and Electronics Engineers, Apr 2019.
The definitive version is available at https://doi.org/10.1109/JOE.2018.2819278
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Convolutional neural networks (CNNs); extreme learning machine (ELM); image segmentation; Markov random fields (MRFs); sidescan sonar; support vector machine (SVM)
International Standard Serial Number (ISSN)
0364-9059
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Apr 2019
Comments
National Basic Research Program of China (973 Program), Grant 2016YFC0301400