mlsanityData to 7 Oct 2026

Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery

arXiv 2201.07124 ↗First posted 18 Jan 2022cs.CVcited by 7

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Abstract (arXiv)

Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearance, obvious intraclass variation, small size and serious background's interference. In this paper, a single-shot detector namely Attentional Feature Refinement and Alignment Network (AFRAN) is proposed for detecting aircraft in SAR images with competitive accuracy and speed. Specifically, three significant components including Attention Feature Fusion Module (AFFM), Deformable Lateral Connection Module (DLCM) and Anchor-guided Detection Module (ADM), are carefully designed in our method for refining and aligning informative characteristics of aircraft. To represent characteristics of aircraft with less interference, low-level textural and high-level semantic features of aircraft are fused and refined in AFFM throughly. The alignment between aircraft's discrete back-scatting points and convolutional sampling spots is promoted in DLCM. Eventually, the locations of aircraft are predicted precisely in ADM based on aligned features revised by refined anchors. To evaluate the performance of our method, a self-built SAR aircraft sliced dataset and a large scene SAR image are collected. Extensive quantitative and qualitative experiments with detailed analysis illustrate the effectiveness of the three proposed components. Furthermore, the topmost detection accuracy and competitive speed are achieved by our method compared with other domain-specific,e.g., DAPN, PADN, and general CNN-based methods,e.g., FPN, Cascade R-CNN, SSD, RefineDet and RPDet.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 30 of its 59 references that are in the map. First posted on arXiv 18 Jan 2022.

Builds on

30 of 59 references.

References · 30

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18 more
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 0; 2025: 2.

7 papers.

  1. 2022Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defensecited by 14
  2. 2023Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognitioncited by 6
  3. 2023LDConv: Linear deformable convolution for improving convolutional neural networkscited by 6
  4. 2023MS-Net: A Multi-modal Self-supervised Network for Fine-Grained Classification of Aircraft in SAR Imagescited by 0
  5. 2023Multi-scale direction-aware SAR object detection network via global information fusioncited by 0
  6. 2025MTSGL: Multi-Task Structure Guided Learning for Robust and Interpretable SAR Aircraft Recognitioncited by 0
  7. 2025Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognitioncited by 0