arXiv Computer Vision

SAR-TS-BERT: Physics-Guided Self-Supervised Pre-Training With Polarimetric Features for SAR Time-Series Crop Classification

SAR-TS-BERT is a physics‑guided, BERT‑style self‑supervised pre‑training framework designed for multitemporal synthetic aperture radar (SAR) crop classification. It fuses backscatter intensities, polarimetric ratios, and Cloude‑Pottier decomposition parameters into a six‑band input, employing a span‑masking strategy that targets key phenological transitions and a dual‑branch Transformer encoder with cross‑attention fusion. The method achieves high accuracies—96.78 % on Estonia’s EuroCropsML and 87.29 % on the USA’s California CDL—while significantly improving performance with limited labeled data.

Hugging Face Trending Papers

Aug 11

SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

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.

multimodal benchmarks

arXiv Machine Learning

Aug 4

PhenoStitch: Training-Free Panoptic Crop Mapping from Satellite Image Time Series

arXiv:2608. 00870v1 Announce Type: cross Abstract: Panoptic crop mapping requires both delineating individual agricultural parcels and assigning a crop type to each parcel from satellite image time series.

By Xuechen Li

computer-vision benchmarks

arXiv Machine Learning

Jun 25

An iterative energy-based multimodal transformer for joint retrieval of wheat soil moisture, leaf area index, and plant height from Sentinel-1 and Sentinel-2 time series

arXiv:2606. 25174v1 Announce Type: new Abstract: Field-scale retrieval of surface soil moisture (SM), leaf area index (LAI), and plant height (PH) is essential for precision agriculture, yet it remains an ill-posed inverse problem.

By Shubham Kumar Singh, Peilei Fan, Suraj A. Yadav, Rajendra Prasad, Prashant K Srivastava

llms multimodal

Hugging Face Trending Papers

Sep 2

Lightweight Adaptation of General-Purpose VLMs for Multispectral and SAR Image Understanding

The paper demonstrates a lightweight method to adapt general-purpose vision‑language models (VLMs) for multispectral and synthetic aperture radar (SAR) image understanding. By rendering each observation as five optical views and one SAR view, naming them in the prompt, and applying LoRA to the language network and selected visual transformer blocks, the authors enable VLMs to process band composites, spectral indices, and radar backscatter without retraining a new foundation model. On a balanced six‑class land‑cover benchmark from BigEarthNet‑v2, the adapted Qwen3‑VL achieves a micro F1 score of 0.8275, and the same protocol improves four other VLMs and transfers to flood verification and captioning tasks.

llms fine-tuning multimodal benchmarks

Hugging Face Trending Papers

3d ago

XuanJian-DINOSAR: A SAR Foundation Model Based on Global and Local Semantic Consistency

Self-supervised pre-training on synthetic aperture radar (SAR) imagery enables transferable representations, but the optical-SAR domain gap and speckle perturbations limit representation stability. Mo...

efficiency benchmarks

arXiv Machine Learning

Sep 10

Cross-modal learning for SAR target recognition using optical vision foundation models

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.

By Lucas Hirsch, James R. Hopgood, Javid Khan, Yoann Altmann, Mike E. Davies

rag computer-vision fine-tuning multimodal safety

arXiv Machine Learning

Sep 30

Cropland PAtteRNS: Parallel Dimensional Attention Networks and Attention to Dataset Disparity for Crop Segmentation in Satellite Imagery Time Series Data

arXiv:2609.38165v1 Announce Type: cross Abstract: The landscape of satellite imagery time series datasets and boundary-pushing architectures for cropland segmentation has never been richer. However,...

By Joseph Metcalfe, Sara Sharifzadeh, Fabio Caraffini

llms computer-vision benchmarks

arXiv AI

Aug 11

SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery

arXiv:2510. 22665v4 Announce Type: replace-cross Abstract: Synthetic Aperture Radar (SAR) is a critical imaging modality due to its all-weather operational capability.

By Qiwei Ma, Xukun Lu, Wang Liu, Puhong Duan, Xudong Kang, Shutao Li

llms computer-vision multimodal benchmarks

arXiv Computer Vision

2d ago

XuanJian-DINOSAR: A SAR Foundation Model Based on Global and Local Semantic Consistency

arXiv:2611.06105v1 Announce Type: new Abstract: Self-supervised pre-training on synthetic aperture radar (SAR) imagery enables transferable representations, but the optical-SAR domain gap and speckle...

By Chunxiao Wu, Huixin Huang, Jialei Pan, Qiang Liang, Tao Wu, Cheng Wu, Long Huang, Yuanji Li, Qiang Cheng, Aifang Liu, Linghao Xia, Rui Zhang

efficiency benchmarks

arXiv Machine Learning

Jul 7

Probing Geospatial SSL Representations with Environmental Signals

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.

By Rohita Mocharla, Vishal M. Patel

benchmarks

arXiv Computer Vision

Sep 24

Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series

arXiv:2409.13568v3 Announce Type: replace Abstract: Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing method...

By Foivos I. Diakogiannis, Zheng-Shu Zhou, Jeff Wang, Gonzalo Mata, Dave Henry, Roger Lawes, Amy Parker, Peter Caccetta, Suzanne Furby, Rodrigo Ibata, Ondrej Hlinka, Jonathan Richetti, Kathryn Batchelor, Chris Herrmann, Andrew Toovey, John Taylor

llms computer-vision benchmarks

Hugging Face Trending Papers

Jul 6

Probing Geospatial SSL Representations with Environmental Signals

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.

benchmarks

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