Superellipse fitting for the classification of mine-like shapes in side-scan sonar images

2003 ◽  
Author(s):  
E. Dura ◽  
J.M. Bell ◽  
D.M. Lane
Keyword(s):  
Side Scan Sonar ◽  
Sonar Images

scholarly journals Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 29416-29428
Author(s):  
Xiaoming Qin ◽  
Xiaowen Luo ◽  
Ziyin Wu ◽  
Jihong Shang
Keyword(s):  
Deep Learning ◽  
Side Scan Sonar ◽  
Sediment Classification ◽  
Sonar Images ◽  
Small Side

scholarly journals Automated Approach To Classification Of Mine-Like Objects Using Multiple-Aspect Sonar Images

2014 ◽  
Vol 4 (2) ◽  
pp. 133-148 ◽  
Author(s):  
Xiaoguang Wang ◽  
Xuan Liu ◽  
Nathalie Japkowicz ◽  
Stan Matwin
Keyword(s):  
Image Recognition ◽  
Class Imbalance ◽  
Experimental Results ◽  
Class Imbalance Problem ◽  
Side Scan Sonar ◽  
Single View ◽  
Imbalance Problem ◽  
Sonar Images ◽  
Multiple Aspect

Abstract In this paper, the detection of mines or other objects on the seabed from multiple side-scan sonar views is considered. Two frameworks are provided for this kind of classification. The first framework is based upon the Dempster–Shafer (DS) concept of fusion from a single-view kernel-based classifier and the second framework is based upon the concepts of multi-instance classifiers. Moreover, we consider the class imbalance problem which is always presents in sonar image recognition. Our experimental results show that both of the presented frameworks can be used in mine-like object classification and the presented methods for multi-instance class imbalanced problem are also effective in such classification.

Sign in / Sign up

Export Citation Format

Share Document