# FSCE: A Target-Aware Frequency-Spatial Collaborative Enhancement Framework for Noise-Resilient SAR ATR

arXiv: https://arxiv.org/abs/2603.21565 · HTML: https://mlsanity.com/paper/2603.21565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 16 of 16)

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- [Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition](https://mlsanity.com/paper/2207.11518/) (2022)
- [Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning](https://mlsanity.com/paper/2001.01425/) (2020)
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- [3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.10795/) (2025)
- [Physics-Guided Detector for SAR Airplanes](https://mlsanity.com/paper/2411.12301/) (2024)
- [MR-Align: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models](https://mlsanity.com/paper/2510.24794/) (2025)
- [Knowledge Distillation for Object Detection: from generic to remote sensing datasets](https://mlsanity.com/paper/2307.09264/) (2023)
- [Time-space-frequency feature Fusion for 3-channel motor imagery classification](https://mlsanity.com/paper/2304.01461/) (2023)

## Abstract (arXiv)

Synthetic aperture radar automatic target recognition (SAR ATR) is severely challenged by coherent speckle noise, whose interference can be progressively amplified by hierarchical nonlinear transformations and eventually damage high-level semantic representations. To address this issue, we propose a Target-Aware Frequency-Spatial Collaborative Enhancement (FSCE) framework for noise-resilient SAR ATR, which integrates frequency-spatial modeling for early feature stabilization with semantic regularization. Specifically, we design a Frequency-Spatial Early-stage Adaptive Enhancement (FS-EAE) module at the network entrance to suppress noise propagation and preserve target structures through collaborative spatial-frequency modeling. Building upon stabilized shallow representation, we further introduce an Adaptive Policy-driven Semantic Alignment (APSA) mechanism, which uses an online teacher policy to impose top-down semantic constraints on the student and feeds semantic guidance back to the enhanced early features during training. Experiments on MSTAR, OpenSARShip, and FUSARShip demonstrate the effectiveness of this synergy. Moreover, the competitive performance of our lightweight impletation FSCE-Netμ with only 0.17M parameters suggests that the proposed framework is applicable to both high-capacity and lightweight architectures.