Multi-Level Heterogeneous Knowledge Transfer Network on Forward Scattering Center Model for Limited Samples SAR ATR

• URL: http://arxiv.org/abs/2509.23596v1
• Date: Sun, 28 Sep 2025 03:04:04 GMT
• Title: Multi-Level Heterogeneous Knowledge Transfer Network on Forward Scattering Center Model for Limited Samples SAR ATR
• Authors: Chenxi Zhao, Daochang Wang, Siqian Zhang, Gangyao Kuang,
• Abstract summary: This work explores a new simulated data to migrate purer and key target knowledge, i.e., forward scattering center model (FSCM)<n>To achieve this purpose, multi-level heterogeneous knowledge transfer network is proposed, which fully migrates FSCM knowledge from the feature, distribution and category levels.<n> Notably, extensive experiments on two new datasets formed by FSCM data and measured SAR images demonstrate the superior performance of our method.
• Score: 10.701687030427422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulated data-assisted SAR target recognition methods are the research hotspot currently, devoted to solving the problem of limited samples. Existing works revolve around simulated images, but the large amount of irrelevant information embedded in the images, such as background, noise, etc., seriously affects the quality of the migrated information. Our work explores a new simulated data to migrate purer and key target knowledge, i.e., forward scattering center model (FSCM) which models the actual local structure of the target with strong physical meaning and interpretability. To achieve this purpose, multi-level heterogeneous knowledge transfer (MHKT) network is proposed, which fully migrates FSCM knowledge from the feature, distribution and category levels, respectively. Specifically, we permit the more suitable feature representations for the heterogeneous data and separate non-informative knowledge by task-associated information selector (TAIS), to complete purer target feature migration. In the distribution alignment, the new metric function maximum discrimination divergence (MDD) in target generic knowledge transfer (TGKT) module perceives transferable knowledge efficiently while preserving discriminative structure about classes. Moreover, category relation knowledge transfer (CRKT) module leverages the category relation consistency constraint to break the dilemma of optimization bias towards simulation data due to imbalance between simulated and measured data. Such stepwise knowledge selection and migration will ensure the integrity of the migrated FSCM knowledge. Notably, extensive experiments on two new datasets formed by FSCM data and measured SAR images demonstrate the superior performance of our method.

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