False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier
In this paper, a new-brand feature-based detector via an improved concave hull classifier (FB-ICHC) is proposed to detect marine small targets. The dimension of feature space is suggested to be three, making a compromise between high detection accuracy and low computational cost. The main contributi...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1808 |
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| author | Sainan Shi Jiajun Wang Jie Wang Tao Li |
| author_facet | Sainan Shi Jiajun Wang Jie Wang Tao Li |
| author_sort | Sainan Shi |
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| description | In this paper, a new-brand feature-based detector via an improved concave hull classifier (FB-ICHC) is proposed to detect marine small targets. The dimension of feature space is suggested to be three, making a compromise between high detection accuracy and low computational cost. The main contributions are in the following two aspects. On the one hand, three features are well-designed from time series and Doppler spectrum, called relative phase zero ratio (RPZR), relative variation coefficient (RCV), and whitened peak height ratio (WPHR). RPZR can measure the pseudo-period properties in phase time series, insensitive to SCRs. In the Doppler spectrum, RCV reflects fluctuation variation in high SCR cases and WPHR describes the intensity property after clutter suppression in low SCR cases. On the other hand, in 3D feature space, an improved concave hull classifier is developed to further shrink the decision region, where a fast two-stage parameter search is designed for low computational cost and accurate control of false alarm rate. Finally, experimental results using open-recognized datasets show that the proposed FB-ICHC detector can improve detection performance by over 20% and reduce runtime by over 49%, compared with existing feature-based detectors with three features. |
| format | Article |
| id | doaj-art-d7f242b39f7a4a71ac14bbdefe41b31c |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-d7f242b39f7a4a71ac14bbdefe41b31c2025-08-20T02:23:44ZengMDPI AGRemote Sensing2072-42922025-05-011711180810.3390/rs17111808False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull ClassifierSainan Shi0Jiajun Wang1Jie Wang2Tao Li3School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaIn this paper, a new-brand feature-based detector via an improved concave hull classifier (FB-ICHC) is proposed to detect marine small targets. The dimension of feature space is suggested to be three, making a compromise between high detection accuracy and low computational cost. The main contributions are in the following two aspects. On the one hand, three features are well-designed from time series and Doppler spectrum, called relative phase zero ratio (RPZR), relative variation coefficient (RCV), and whitened peak height ratio (WPHR). RPZR can measure the pseudo-period properties in phase time series, insensitive to SCRs. In the Doppler spectrum, RCV reflects fluctuation variation in high SCR cases and WPHR describes the intensity property after clutter suppression in low SCR cases. On the other hand, in 3D feature space, an improved concave hull classifier is developed to further shrink the decision region, where a fast two-stage parameter search is designed for low computational cost and accurate control of false alarm rate. Finally, experimental results using open-recognized datasets show that the proposed FB-ICHC detector can improve detection performance by over 20% and reduce runtime by over 49%, compared with existing feature-based detectors with three features.https://www.mdpi.com/2072-4292/17/11/1808sea cluttersmall target detectionfeature extractionconcave hull classifier |
| spellingShingle | Sainan Shi Jiajun Wang Jie Wang Tao Li False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier Remote Sensing sea clutter small target detection feature extraction concave hull classifier |
| title | False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier |
| title_full | False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier |
| title_fullStr | False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier |
| title_full_unstemmed | False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier |
| title_short | False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier |
| title_sort | false alarm controllable detection of marine small targets via improved concave hull classifier |
| topic | sea clutter small target detection feature extraction concave hull classifier |
| url | https://www.mdpi.com/2072-4292/17/11/1808 |
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