Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

Yongxu Li; Xudong Lai; Mingwei Wang

doi:10.1007/s13131-021-1980-2

Volume 41 Issue 7

Jul. 2022

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Yongxu Li, Xudong Lai, Mingwei Wang. Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images[J]. Acta Oceanologica Sinica, 2022, 41(7): 180-192. doi: 10.1007/s13131-021-1980-2

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Yongxu Li, Xudong Lai, Mingwei Wang. Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images[J]. Acta Oceanologica Sinica, 2022, 41(7): 180-192. doi: 10.1007/s13131-021-1980-2

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Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

doi: 10.1007/s13131-021-1980-2

1.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2.
School of Computer Science, Hubei University of Technology, Wuhan 430068, China

Funds: The National Natural Science Foundation of China under contract No. 61971455.

More Information

Corresponding author: E-mail: laixudong@whu.edu.cn
Received Date: 2021-04-09
Accepted Date: 2021-12-27

Available Online: 2022-02-28

Publish Date: 2022-07-08

Abstract

Abstract

Ship detection using synthetic aperture radar (SAR) plays an important role in marine applications. The existing methods are capable of quickly obtaining many candidate targets, but numerous non-ship objects may be wrongly detected in complex backgrounds. These non-ship false alarms can be excluded by training discriminators, and the desired accuracy is obtained with enough verified samples. However, the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation, which is difficult and time consuming. To address this issue, a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets. Specifically, various features commonly used in SAR image target discrimination are extracted, and several acknowledged classification models and their classic variants are investigated. Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model. Then, the performance of all the discriminators is tested, and better discriminators are selected for implementing the semisupervised training process. These strategies enhance the diversity and reliability of the discriminators, and their heterogeneous ensemble makes more correct judgments on candidate targets, which facilitates further positive training. Experimental results demonstrate that the proposed method outperforms traditional tri-training.
- synthetic aperture radar,
- ship target discrimination,
- non-ship false alarms,
- semisupervised,
- heterogeneous ensemble

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References(51)

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