Graph-Based Maximum Connected-Component Learning Algorithm for Small Target Detection in Maritime Radars

Anomaly detection needs to learn one-class classifiers from normal instances in observation or feature spaces. In the Neyman–Pearson criterion, the design of one-class classifiers boils down to finding the minimal-volume decision region subject to the error probability of normal instances no larger...

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Veröffentlicht in:	IEEE transactions on aerospace and electronic systems Jg. 61; H. 1; S. 250 - 265
Hauptverfasser:	Bai, Xiaohui, Xu, Shuwen, Guo, Zixun, Shui, Penglang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Anomalies Anomaly detection Classification algorithms convexhull learning algorithm Convexity Detectors False alarms Feature extraction feature-based detection Graph theory graph-based maximum connected-component (GMCC) learning algorithm Level set Machine learning Probability density functions Radar sea-surface small target detection Search algorithms Signal processing algorithms Target detection Training
ISSN:	0018-9251, 1557-9603
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Abstract	Anomaly detection needs to learn one-class classifiers from normal instances in observation or feature spaces. In the Neyman–Pearson criterion, the design of one-class classifiers boils down to finding the minimal-volume decision region subject to the error probability of normal instances no larger than a desired false alarm rate. The theoretical solution to this design problem is the probability density function (pdf) level set of normal instances. In low-dimensional feature spaces, by combining training samples with the convexity regularity on decision regions, the convexhull learning algorithm is a technique for designing one-class classifiers. In order to overcome its dimension limitation and the mismatch of convexity to the level sets of a multimodal pdf, this article considers the approach to replace the convexity by the connectivity to regularize decision regions. A fast graph-based maximum connected-component learning algorithm is proposed to design one-class classifiers in high-dimensional feature spaces, which exploits the fast maximum connected-component search algorithm in a large-scale undirected graph. Moreover, for the application of sea-surface small target detection, the proposed algorithm combines ten-dimensional features to design feature-based detectors. Experimental results on the recognized radar database indicate the effectiveness of the proposed algorithm.
AbstractList	Anomaly detection needs to learn one-class classifiers from normal instances in observation or feature spaces. In the Neyman–Pearson criterion, the design of one-class classifiers boils down to finding the minimal-volume decision region subject to the error probability of normal instances no larger than a desired false alarm rate. The theoretical solution to this design problem is the probability density function (pdf) level set of normal instances. In low-dimensional feature spaces, by combining training samples with the convexity regularity on decision regions, the convexhull learning algorithm is a technique for designing one-class classifiers. In order to overcome its dimension limitation and the mismatch of convexity to the level sets of a multimodal pdf, this article considers the approach to replace the convexity by the connectivity to regularize decision regions. A fast graph-based maximum connected-component learning algorithm is proposed to design one-class classifiers in high-dimensional feature spaces, which exploits the fast maximum connected-component search algorithm in a large-scale undirected graph. Moreover, for the application of sea-surface small target detection, the proposed algorithm combines ten-dimensional features to design feature-based detectors. Experimental results on the recognized radar database indicate the effectiveness of the proposed algorithm.
Author	Xu, Shuwen Shui, Penglang Bai, Xiaohui Guo, Zixun
Author_xml	– sequence: 1 givenname: Xiaohui orcidid: 0000-0001-7936-1332 surname: Bai fullname: Bai, Xiaohui email: xhbai@stu.xidian.edu.cn organization: National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, China – sequence: 2 givenname: Shuwen orcidid: 0000-0002-3557-5897 surname: Xu fullname: Xu, Shuwen email: swxu@mail.xidian.edu.cn organization: National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, China – sequence: 3 givenname: Zixun orcidid: 0000-0002-7327-4382 surname: Guo fullname: Guo, Zixun email: zxguo@nwpu.edu.cn organization: School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China – sequence: 4 givenname: Penglang orcidid: 0000-0002-5921-5255 surname: Shui fullname: Shui, Penglang email: plshui@xidian.edu.cn organization: National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, China
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SubjectTerms	Algorithms Anomalies Anomaly detection Classification algorithms convexhull learning algorithm Convexity Detectors False alarms Feature extraction feature-based detection Graph theory graph-based maximum connected-component (GMCC) learning algorithm Level set Machine learning Probability density functions Radar sea-surface small target detection Search algorithms Signal processing algorithms Target detection Training
Title	Graph-Based Maximum Connected-Component Learning Algorithm for Small Target Detection in Maritime Radars
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