The paper introduces FSCE, a Target‑Aware Frequency‑Spatial Collaborative Enhancement framework designed to improve noise‑resilient synthetic aperture radar (SAR) automatic target recognition (ATR). It combines a Frequency‑Spatial Early‑stage Adaptive Enhancement module that suppresses speckle noise while preserving target structures, with an Adaptive Policy‑driven Semantic Alignment mechanism that applies top‑down semantic constraints during training. Experiments on MSTAR, OpenSARShip, and FUSARShip datasets show that the lightweight FSCE‑Net_µ, with only 0.17 M parameters, achieves competitive performance across both high‑capacity and lightweight architectures.
arXiv:2607.23238v3 Announce Type: replace Abstract: Masked image modeling has become a dominant paradigm for SAR pre-training, yet the design of the reconstruction target remains fundamentally unsett...
SARES-DEIM is a SAR ship detection framework that builds on the DETR architecture. It introduces a sparse mixture‑of‑experts module, SARESMoE, which routes features to wavelet, spatial, and frequency experts, and a Space‑to‑Depth Enhancement Pyramid (SDEP) neck that preserves high‑resolution cues for small targets. The method achieves state‑of‑the‑art performance on HRSID and SAR‑Ship‑Dataset, delivering 76.4% mAP50:95 and 93.8% mAP50 while maintaining efficient inference at 136.5 FPS on an NVIDIA A40 GPU.
SAREO‑FM is a foundation model that separates semantic supervision from reconstruction supervision in synthetic aperture radar (SAR) and electro‑optical (EO) imagery. It uses modality tokens for masked reconstruction and learnable semantic queries guided by a pretrained vision foundation model to capture scene content. Trained on the million‑scale SAR‑1M corpus, the model shows strong unimodal transfer for SAR and EO alone and significant improvements when both modalities are combined.
arXiv:2609.37496v1 Announce Type: new Abstract: Paired synthetic aperture radar (SAR) and electro-optical (EO) imagery is increasingly available across sensors, resolutions, and geographic regions. Y...
ProSR is a new method for Synthetic Aperture Radar (SAR) super‑resolution that treats the task as a discrete token prediction problem in a quantized latent space. By mapping SAR signal features to discrete scattering primitives and using a self‑supervised backbone to extract label‑free semantic priors, ProSR preserves the impulsive, physically consistent scattering statistics of SAR images. The approach includes Semantic‑Aligned Detail Encoding and a Prototype‑Map‑Guided Attention mechanism, and it has been validated on a large‑scale 0.25 m resolution benchmark from the Umbra Open Dataset, achieving superior visual quality while maintaining essential scattering characteristics.
The paper introduces BMT, a unified hierarchical Vision Transformer that jointly performs SAR-to-optical image translation and semantic segmentation. It incorporates a LocalViTBlock, an enhanced output module, a ControlNet-style conditional injection, and a bounded Kendall uncertainty weighting scheme to balance the two tasks. Experiments on paired and unpaired datasets demonstrate competitive performance in both translation quality and segmentation accuracy.
arXiv:2607. 16819v1 Announce Type: new Abstract: In recent years, large-scale vision-language models have been driving a paradigm shift in intelligent remote sensing image interpretation.
arXiv:2602. 19190v4 Announce Type: replace-cross Abstract: Research on the intelligent interpretation of all-weather, all-time Synthetic Aperture Radar (SAR) is crucial for advancing remote sensing applications.
arXiv:2510. 22665v4 Announce Type: replace-cross Abstract: Synthetic Aperture Radar (SAR) is a critical imaging modality due to its all-weather operational capability.
arXiv:2610.27757v1 Announce Type: new Abstract: Synthetic aperture radar (SAR) and optical imagery provide complementary structural and appearance information for remote sensing object detection. How...
The paper introduces SIFR, a frequency-aware dual‑stream framework for domain‑generalized semantic segmentation. SIFR performs spectral‑guided invariant fusion of frozen CLIP and DINOv2 features to suppress domain‑sensitive responses while preserving semantic and structural cues, and adds a spectral decoder refinement module that normalizes low‑frequency statistics and adaptively selects frequencies to reduce residual domain bias. Experiments on synthetic‑to‑real, real‑to‑real, and adverse‑weather benchmarks show consistent improvements over prior state‑of‑the‑art methods.
arXiv:2606. 31609v1 Announce Type: cross Abstract: Radar sensors provide reliable perception under adverse weather and lighting conditions, but their sparse, noisy, and weakly semantic measurements make dense semantic segmentation challenging.