Next Article in Journal
Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion
Next Article in Special Issue
Operational Monitoring of Illegal Fishing in Ghana through Exploitation of Satellite Earth Observation and AIS Data
Previous Article in Journal
Pre-Constrained Machine Learning Method for Multi-Year Mapping of Three Major Crops in a Large Irrigation District
Previous Article in Special Issue
Detection, Localization and Classification of Multiple Mechanized Ocean Vessels over Continental-Shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

NanJing Research Institute of Surveying, Mapping & Geotechnical Investigation, Co. Ltd., Nanjing 210019, China
School of Geomatics and Marine Information, Huaihai Institute of Technology, 59 Cangwu Road, Lianyungang 222005, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 243;
Received: 25 December 2018 / Revised: 21 January 2019 / Accepted: 21 January 2019 / Published: 24 January 2019
(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)
PDF [11229 KB, uploaded 24 January 2019]
  |     |  


In order to realize the automatic and accurate recognition of shipwreck targets in side-scan sonar (SSS) waterfall images, a pipeline that contains feature extraction, selection, and shipwreck recognition, an AdaBoost model was constructed by sample images. Shipwreck targets are detected quickly by a nonlinear matching model, and a shipwreck recognition in SSS waterfall images are given, and according to a wide set of combinations of different types of these individual procedures, the model is able to recognize the shipwrecks accurately. Firstly, two feature-extraction methods suitable for recognizing SSS shipwreck targets from natural sea bottom images were studied. In addition to these two typical features, some commonly used features were extracted and combined as comprehensive features to characterize shipwrecks from various feature spaces. Based on Independent Component Analysis (ICA), the preferred features were selected from the comprehensive features, which avoid dimension disaster and improved the correct recognition rate. Then, the Gentle AdaBoost algorithm was studied and used for constructing the shipwreck target recognition model using sample images. Finally, a shipwreck target recognition process for the SSS waterfall image was given, and the process contains shipwreck target fast detection by a nonlinear matching model and accurate recognition by the Gentle AdaBoost recognition model. The results show that the correct recognition rate of the model for the sample image is 97.44%, while the false positive rate is 3.13% and the missing detection rate is 0. This study of a measured SSS waterfall image confirms the correctness of the recognition process and model. View Full-Text
Keywords: Side Scan Sonar; feature extraction; independent component analysis; Gentle AdaBoost; shipwreck target recognition Side Scan Sonar; feature extraction; independent component analysis; Gentle AdaBoost; shipwreck target recognition

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zhu, B.; Wang, X.; Chu, Z.; Yang, Y.; Shi, J. Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image. Remote Sens. 2019, 11, 243.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top