基于熵权-可信度因子的舰船细粒度分类方法

doi:10.12305/j.issn.1001-506X.2025.11.06

Abstract

Abstract:

For the fine-grained ship target classification in multi-payload collaborative detection task, considering the various revisit time of payloads, a method based on entropy weight and credibility model for fine grained classification of ship targets is proposed. With the support of historical information, the uncertainty of multiple electronic/imaging detection payloads are evaluated through entropy weight.Then, a certainty factor model is established based on weighted synthesis rules. The proposed method solves the interruption of reasoning issues caused by intersection of detection windows via combininng regional historical situation, achieving decision-level cross-modal information fusion. In the fine-grained classification task involving six categories of ship targets, the accuracy of the proposed method achieves 91%, demonstrating satisfactory accuracy and robustness.

Key words: information fusion, fine-grained classification, eletronic detection, imaging detection

CLC Number:

TP 391

Xiaozhou ZHANG, Zhenjun JIA, Xiaoyun GUO. Method for fine-grained ship classification based on entropy weight and certainty factor[J]. Systems Engineering and Electronics, 2025, 47(11): 3568-3573.

Figures/Tables 6

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类别	$ P1 $	$ P2 $	$ P3 $	$ P4 $	$ P5 $	$ P6 $
长宽比	8.00	7.50	6.00	7.00	6.50	7.00

指标	电侦卫星单一信源	成像卫星单一信源	本文方法	证据合成理论	Bayes理论
准确率	0.580 0	0.760 0	0.910 0	0.750 0	0.830 0
精确率	0.228 9	0.659 7	0.839 2	0.519 9	0.621 7
召回率	0.201 1	0.620 9	0.834 3	0.490 3	0.596 9
F1值	0.214 1	0.639 7	0.836 8	0.504 7	0.608 4

指标	VAR	MAD	RMS	IQR
准确率	0.001 4	0.029 4	0.851 3	0.050 0
精确率	0.005 5	0.060 0	0.740 9	0.104 4
召回率	0.006 0	0.062 6	0.728 0	0.106 8
F1值	0.005 7	0.061 3	0.734 4	0.107 0

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Method for fine-grained ship classification based on entropy weight and certainty factor

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