HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensing

arXiv:2609.37340 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/facebookresearch/sam32https:

Terminology

• Foundation Model — 23× in this paper · explained in 3 episodes
• Abundance — 20× in this paper · explained in 1 episode
• Adaptation — 18× in this paper · explained in 1 episode
• Benchmark — 17× in this paper · explained in 2 episodes
• Consistency — 13× in this paper · explained in 3 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Transformer — 12× in this paper · explained in 2 episodes
• Ablation — 11× in this paper · explained in 1 episode
• Foundation Models — 10× in this paper · explained in 7 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Pseudo-labels — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Sparsity — 5× in this paper · explained in 4 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Mask Generation — 4× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Transferability — 3× in this paper · explained in 4 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Aperture — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications
• Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
• SAM 3: Segment Anything with Concepts
• DINOv3
• HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods
• SpaceNet: A Remote Sensing Dataset and Challenge Series
• Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
• Carbon Emissions and Large Neural Network Training

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