Rethinking SAR ATR: A Target-Aware Frequency-Spatial Enhancement Framework with Noise-Resilient Knowledge Guidance

• URL: http://arxiv.org/abs/2603.21565v1
• Date: Mon, 23 Mar 2026 04:35:31 GMT
• Title: Rethinking SAR ATR: A Target-Aware Frequency-Spatial Enhancement Framework with Noise-Resilient Knowledge Guidance
• Abstract summary: This paper proposes a target-aware frequency-spatial enhancement framework with noise-resilient knowledge guidance (FSCE) for SAR target recognition.<n>The proposed framework incorporates a frequency-spatial shallow feature adaptive enhancement (DSAF) module, which processes shallow features through spatial multi-scale convolution and frequency-domain wavelet convolution.<n>Through the collaborative optimization of attention transfer and noise-resilient representation learning, the proposed approach significantly improves the stability of target recognition under noisy conditions.
• Score: 2.0394716255215664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic aperture radar automatic target recognition (SAR ATR) is of considerable importance in marine navigation and disaster monitoring. However, the coherent speckle noise inherent in SAR imagery often obscures salient target features, leading to degraded recognition accuracy and limited model generalization. To address this issue, this paper proposes a target-aware frequency-spatial enhancement framework with noise-resilient knowledge guidance (FSCE) for SAR target recognition. The proposed framework incorporates a frequency-spatial shallow feature adaptive enhancement (DSAF) module, which processes shallow features through spatial multi-scale convolution and frequency-domain wavelet convolution. In addition, a teacher-student learning paradigm combined with an online knowledge distillation method (KD) is employed to guide the student network to focus more effectively on target regions, thereby enhancing its robustness to high-noise backgrounds. Through the collaborative optimization of attention transfer and noise-resilient representation learning, the proposed approach significantly improves the stability of target recognition under noisy conditions. Based on the FSCE framework, two network architectures with different performance emphases are developed: lightweight DSAFNet-M and high-precision DSAFNet-L. Extensive experiments are conducted on the MSTAR, FUSARShip and OpenSARShip datasets. The results show that DSAFNet-L achieves competitive or superior performance compared with various methods on three datasets; DSAFNet-M significantly reduces the model complexity while maintaining comparable accuracy. These results indicate that the proposed FSCE framework exhibits strong cross-model generalization.

Related papers

• Iterative Inference-time Scaling with Adaptive Frequency Steering for Image Super-Resolution [75.3690742776891]
  We propose Iterative Diffusion Inference-Time Scaling with Adaptive Frequency Steering (IAFS)<n>IAFS addresses the challenge of balancing perceptual quality and structural fidelity by progressively refining the generated image through iterative correction of structural deviations.<n>Experiments show that IAFS effectively resolves the perception-fidelity conflict, yielding consistently improved perceptual detail and structural accuracy, and outperforming existing inference-time scaling methods.
  arXiv  Detail & Related papers  (2025-12-29T15:09:20Z)
• Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation [45.347078403677216]
  Large-scale models (LSMs) can be an effective framework for semantic representation and understanding.<n>However, their direct deployment is often hindered by high computational complexity and resource requirements.<n>This paper proposes a novel knowledge distillation based semantic communication framework.
  arXiv  Detail & Related papers  (2025-08-04T07:47:18Z)
• SWAN: Synergistic Wavelet-Attention Network for Infrared Small Target Detection [8.098063209250684]
  Infrared small target detection (IRSTD) is critical in both civilian and military applications.<n>Recent methods focus on conventional convolution operations, which primarily capture local spatial patterns.<n>We propose the Synergistic Wavelet-Attention Network (SWAN), a novel framework designed to perceive targets from both spatial and frequency domains.
  arXiv  Detail & Related papers  (2025-08-02T11:26:58Z)
• FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution [70.61549422952193]
  Face super-resolution (FSR) under limited computational costs remains an open problem.<n>Existing approaches typically treat all facial pixels equally, resulting in suboptimal allocation of computational resources.<n>We propose FADPNet, a Frequency-Aware Dual-Path Network that decomposes facial features into low- and high-frequency components.
  arXiv  Detail & Related papers  (2025-06-17T02:33:42Z)
• Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition [13.783950035836593]
  We propose a few-shot class-incremental learning (FSCIL) framework based on a dual-branch architecture that focuses on local feature extraction.<n>This incorporates a lightweight cross-attention mechanism that fuses domain-specific features with global dependencies to ensure robust feature interaction.<n> Experimental results on the MSTAR benchmark dataset demonstrate that the proposed framework consistently outperforms state-of-the-art methods in FSCIL SAR-ATR.
  arXiv  Detail & Related papers  (2025-05-26T06:25:30Z)
• EMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Propagation for SAR Target Recognition [4.251056028888424]
  This study proposes a physically explainable framework for complex-valued SAR image recognition.<n>The network architecture is fully parameterized, with all learnable parameters endowed with clear physical meanings.<n>The proposed method possesses a strong physical decision logic, high physical explainability and robustness, as well as excellent de-aliasing capabilities.
  arXiv  Detail & Related papers  (2024-10-13T07:04:49Z)
• IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target Recognition [13.783950035836593]
  IncSAR is an incremental learning framework designed to tackle catastrophic forgetting in target recognition.<n>To mitigate the speckle noise inherent in SAR images, we employ a denoising module based on a neural network approximation.<n>Experiments on the MSTAR, SAR-AIRcraft-1.0, and OpenSARShip benchmark datasets demonstrate that IncSAR significantly outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-10-08T08:49:47Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective Learning [6.60571587618006]
  Radio speech echo is a specific phenomenon in the air traffic control (ATC) domain, which degrades speech quality and impacts automatic speech recognition (ASR) accuracy. In this work, a time-domain recognition-oriented speech enhancement framework is proposed to improve speech intelligibility and advance ASR accuracy. The framework serves as a plug-and-play tool in ATC scenarios and does not require additional retraining of the ASR model.
  arXiv  Detail & Related papers  (2023-12-11T04:51:41Z)
• You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment [45.62136459502005]
  We propose a network to perform full reference (FR) and no reference (NR) IQA. We first employ an encoder to extract multi-level features from input images. A Hierarchical Attention (HA) module is proposed as a universal adapter for both FR and NR inputs. A Semantic Distortion Aware (SDA) module is proposed to examine feature correlations between shallow and deep layers of the encoder.
  arXiv  Detail & Related papers  (2023-10-14T11:03:04Z)
• Histogram Layer Time Delay Neural Networks for Passive Sonar Classification [58.720142291102135]
  A novel method combines a time delay neural network and histogram layer to incorporate statistical contexts for improved feature learning and underwater acoustic target classification. The proposed method outperforms the baseline model, demonstrating the utility in incorporating statistical contexts for passive sonar target recognition.
  arXiv  Detail & Related papers  (2023-07-25T19:47:26Z)
• ASR: Attention-alike Structural Re-parameterization [53.019657810468026]
  We propose a simple-yet-effective attention-alike structural re- parameterization (ASR) that allows us to achieve SRP for a given network while enjoying the effectiveness of the attention mechanism. In this paper, we conduct extensive experiments from a statistical perspective and discover an interesting phenomenon Stripe Observation, which reveals that channel attention values quickly approach some constant vectors during training.
  arXiv  Detail & Related papers  (2023-04-13T08:52:34Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.