Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are closely coupled with electromagnetic scattering mechanisms. Many existing complex-valued networks can preserve amplitude-phase information, but they are often limited in long-range spatial dependency modeling and usually incorporate polarimetric priors only as input-level or shallow auxiliary features.
arXiv:2607. 19787v1 Announce Type: cross Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are closely coupled with electromagnetic scattering mechanisms.
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.
The paper proposes a cross‑modal framework that uses a frozen DINOv3 optical vision foundation model to create class‑level prototypes for Synthetic Aperture Radar (SAR) target recognition. By aligning SAR embeddings to these optical prototypes, the SAR model learns to classify SAR images without needing paired optical data. Experiments on the UNICORNv2 dataset show that this prototype alignment improves SAR classification accuracy compared to baseline methods and yields clearer class separation in the embedding space.
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.
Agricultural monitoring faces unique challenges, arising from the landscape's complex temporal, phenological, and climate dynamics, yet monitoring them is critical for ensuring food security. Synthetic Aperture Radar (SAR) satellites offer all-weather day-night imaging capability supporting key monitoring tasks including crop type mapping, yield prediction and phenological event detection.
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: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:2607. 05207v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task.
Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task. Most geospatial benchmarks evaluate representations solely through downstream tasks, providing limited insight into the information encoded within the representation itself.
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...
arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.
The paper introduces a physics‑guided deep metric learning approach for open‑world radar pulse de‑interleaving, leveraging continuous Time‑of‑Arrival sinusoidal positional encodings to model physical inter‑pulse durations. It builds on a transformer‑based framework, optimizing network parameters solely with Supervised Contrastive learning and employing physics‑based priors—PRI consistency and AoA continuity—for validation and checkpoint selection via unsupervised HDBSCAN clustering. This method aims to improve de‑interleaving performance in dense, contested electromagnetic environments where classical techniques falter.