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

1
NanJing Research Institute of Surveying, Mapping & Geotechnical Investigation, Co. Ltd., Nanjing 210019, China
2
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; https://doi.org/10.3390/rs11030243
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)
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Abstract

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
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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.

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