mlsanityData to 7 Oct 2026

HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery

arXiv 2503.21841 ↗First posted 27 Mar 2025cs.CVcited by 16

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Abstract (arXiv)

Advanced interpretation of hyperspectral remote sensing images benefits many precise Earth observation tasks. Recently, visual foundation models have promoted the remote sensing interpretation but concentrating on RGB and multispectral images. Due to the varied hyperspectral channels,existing foundation models would face image-by-image tuning situation, imposing great pressure on hardware and time resources. In this paper, we propose a tuning-free hyperspectral foundation model called HyperFree, by adapting the existing visual prompt engineering. To process varied channel numbers, we design a learned weight dictionary covering full-spectrum from 0.4 ~ 2.5 , μm, supporting to build the embedding layer dynamically. To make the prompt design more tractable, HyperFree can generate multiple semantic-aware masks for one prompt by treating feature distance as semantic-similarity. After pre-training HyperFree on constructed large-scale high-resolution hyperspectral images, HyperFree (1 prompt) has shown comparable results with specialized models (5 shots) on 5 tasks and 11 datasets.Code and dataset are accessible at https://rsidea.whu.edu.cn/hyperfree.htm.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 35 of its 91 references that are in the map. First posted on arXiv 27 Mar 2025.

Builds on

35 of 91 references.

References · 35

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2023Segment Anythingcited by 8,158
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  4. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 428
  5. 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 402
  6. 2017Functional Map of the Worldcited by 338
  7. 2018Fully Convolutional Siamese Networks for Change Detectioncited by 305
  8. 2021Remote Sensing Image Change Detection with Transformerscited by 305
  9. 2022Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learningcited by 302
  10. 2023SpectralGPT: Spectral Remote Sensing Foundation Modelcited by 250
  11. 2017Positive-Unlabeled Learning with Non-Negative Risk Estimatorcited by 217
  12. 2023Language Model Tokenizers Introduce Unfairness Between Languagescited by 207
23 more
  1. 2023SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagerycited by 202
  2. 2023RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Modelcited by 180
  3. 2018SpaceNet: A Remote Sensing Dataset and Challenge Seriescited by 178
  4. 2022Advancing Plain Vision Transformer Towards Remote Sensing Foundation Modelcited by 149
  5. 2019HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classificationcited by 91
  6. 2023Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineeringcited by 86
  7. 2024HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Modelcited by 72
  8. 20203D Quasi-Recurrent Neural Network for Hyperspectral Image Denoisingcited by 44
  9. 2025MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classificationcited by 44
  10. 2024SpectralEarth: Training Hyperspectral Foundation Models at Scalecited by 42
  11. 2018Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoisingcited by 39
  12. 2018Material Based Object Tracking in Hyperspectral Videos: Benchmark and Algorithmscited by 36
  13. 2022Spatial-Spectral Transformer for Hyperspectral Image Denoisingcited by 34
  14. 2019SpaceNet MVOI: a Multi-View Overhead Imagery Datasetcited by 29
  15. 2020FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classificationcited by 27
  16. 2024PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Imagescited by 24
  17. 2021Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priorscited by 12
  18. 2023Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Packagecited by 12
  19. 2023GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detectioncited by 9
  20. 2023One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learningcited by 8
  21. 2023Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagerycited by 8
  22. 2022One-Class Risk Estimation for One-Class Hyperspectral Image Classificationcited by 6
  23. 2020Road Network and Travel Time Extraction from Multiple Look Angles with SpaceNet Datacited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 7.

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  1. 2025TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysiscited by 35
  2. 2025GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Datacited by 7
  3. 2025SpectralX: Parameter-efficient Domain Generalization for Spectral Remote Sensing Foundation Modelscited by 5
  4. 2025Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challengescited by 3
  5. 2025FUSE-RSVLM: Feature Fusion Vision-Language Model for Remote Sensingcited by 3
  6. 2026HyperFM: An Efficient Hyperspectral Foundation Model with Spectral Groupingcited by 3
  7. 2025CARL: Camera-Agnostic Representation Learning for Spectral Image Analysiscited by 2
  8. 2025MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Datacited by 2
  9. 2025SpecAware: A Spectral-Content Aware Foundation Model for Unifying Multi-Sensor Learning in Hyperspectral Remote Sensing Mappingcited by 1
  10. 2025Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptabilitycited by 1
  11. 2026Cross-Domain Transfer of Hyperspectral Foundation Modelscited by 0
  12. 2026Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Datacited by 0
4 more
  1. 2026MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Modelscited by 0
  2. 2026HyperImageNet: A Large-Scale High-Spatial Resolution Hyperspectral Imagery Classification Benchmarkcited by 0
  3. 2026HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensingcited by 0
  4. 2026Super-Resolving Unseen Hyperspectral Sensors at Any Scale via Spatial Operatorscited by 0