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Sea-Surface Small Target Detection Based on Joint Graph Features in Dual Channels
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Sainan SHI1, 2, Sutong JIANG1, Jiajun WANG1, Tao LI3, 4
Radar Science and Technology | 2025, 23(5) : 491 - 502
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Radar Science and Technology | 2025, 23(5): 491-502
Sea-Surface Small Target Detection Based on Joint Graph Features in Dual Channels
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Sainan SHI1, 2, Sutong JIANG1, Jiajun WANG1, Tao LI3, 4
Affiliations
  • 1.School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 3.School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
  • 4.Nanjing Research Institute of Electronics Technology, Nanjing 210013, China
doi: 10.3969/j.issn.1672-2337.2025.05.003
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Currently, sea-surface small targets have become the focus and difficulty of marine radar detection. The existing detection methods are limited to the use of unilateral information such as radar echo amplitude or spectrum, making it difficult to effectively detect small targets. Thus, a sea-surface small target detection method using dual-channel joint graph features (DC-JGF) is proposed in this paper. Firstly, the time-domain phase sequence and the frequencydomain amplitude sequence are extracted from the radar complex echo sequence to generate the time-frequency domain dual channel. In each channel, graphs are generated separately to provide rich correlation information. Secondly, in the time domain channel, the largest and the second-largest eigenvalues of graph Laplacian matrix are fused as the first feature to evaluate the graph density. In the frequency domain channel, by extracting non-zero elements from the diagonal of the degree matrix, the entropy value is calculated as the second feature to measure the dispersion of the vertex distribution of the graph. Then, the two features are used as detection statistics to determine whether they fall within the decision region given by the convex hull algorithm with target guidance. The detection results are obtained. Finally, experimental results using measured data demonstrate that the proposed detector can achieve robust and efficient detection performance in complex detection environments.

sea clutter  /  target detection  /  dual channels  /  connected graph  /  feature fusion
Sainan SHI, Sutong JIANG, Jiajun WANG, Tao LI. Sea-Surface Small Target Detection Based on Joint Graph Features in Dual Channels[J]. Radar Science and Technology, 2025 , 23 (5) : 491 -502 . DOI: 10.3969/j.issn.1672-2337.2025.05.003
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doi: 10.3969/j.issn.1672-2337.2025.05.003
  • Receive Date：2024-11-08
  • Online Date：2026-04-23
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  • Received：2024-11-08
  • Revised：2024-12-28
Affiliations
    1.School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
    2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
    3.School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
    4.Nanjing Research Institute of Electronics Technology, Nanjing 210013, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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