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Hyperspectral Image Models: Technical Report
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arXiv Computer Vision
1d ago
Hyperspectral Image Models: Technical Report
The technical report introduces Hyperspectral Image Models, a modular framework that unifies 55 deep‑learning models across six paradigms for hyperspectral remote sensing. It standardizes tensor conventions, evaluation protocols, and dataset handling, integrating 24 benchmark scenes from various sensors and providing tools to avoid train‑test overlap. Experiments across 1,320 model‑scene combinations show that scene difficulty outweighs architecture, with no single paradigm dominating and small models achieving performance comparable to much larger ones.
arXiv Computer Vision
2d ago
HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensing
HyperSAM is a promptable foundation model for hyperspectral remote sensing that integrates a data‑centric synthesis pipeline with a spectral adaptation architecture based on Segment Anything Model 3 (SAM3). The model generates full‑spectrum hyperspectral cubes from high‑resolution multispectral imagery using a physics‑informed abundance‑transfer generator, and employs SAM3‑derived pseudo‑masks for object‑centric supervision. With a frozen SAM3 RGB branch, a trainable hyperspectral encoder, ControlNet‑style feature injection, and a mixture‑of‑experts mask refiner, HyperSAM demonstrates strong generalization across diverse hyperspectral tasks such as classification, anomaly detection, change detection, target detection, and airborne oil‑spill mapping.
arXiv Computer Vision
Aug 31
HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone
HyperVision introduces the first ground‑based hyperspectral pre‑trained backbone, addressing challenges of varying spectral configurations, limited annotations, and dataset diversity. It employs a channel‑adaptive dynamic embedding to unify heterogeneous inputs, a multi‑source pseudo‑labeling strategy combining SAM2 spatial cues with HyperFree spectral details, and cross‑modal knowledge distillation from a pre‑trained RGB vision model. Trained on 15k images from 26 datasets, HyperVision achieves significant improvements—up to 16.3% relative gain in hyperspectral semantic segmentation, 2.1% in object tracking AUC, and 35.5% reduction in salient object detection MAE—while requiring only head‑only adaptation.
arXiv Machine Learning
Sep 3
Ten Architectures, One Error: Shared Failure Modes in Hyperspectral Classification under Spatially Disjoint Evaluation
The paper critiques the common practice of random pixel splits in hyperspectral image classification, noting that such splits allow test pixels to be adjacent to training pixels, inflating accuracy. It proposes a leakage‑free evaluation protocol that enforces spatial separation based on the model’s receptive field and applies it to ten diverse architectures, finding a significant drop in Macro‑F1 (average 0.147) and substantial changes in model rankings. The study also shows that all ten models misclassify the same pixels, indicating a spectral ambiguity in the data that current methods cannot resolve.
arXiv Machine Learning
Jul 28
When Less Is More: A Controlled Benchmark of Lightweight CNNs for Satellite Land-Cover Segmentation on DeepGlobe
arXiv:2607. 23024v1 Announce Type: cross Abstract: High-resolution satellite imagery is the backbone of good land-cover classification, and without that, environmental monitoring, urban planning, and sustainable resource management all fall short.
arXiv Computer Vision
Aug 28
SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation
SIMPLER is a pre‑fine‑tuning method that reduces inference and deployment costs for Earth Observation foundation models by pruning redundant layers. It uses layer‑wise representation similarity on unlabeled task data to identify and remove up to 79% of parameters without requiring gradients, magnitude heuristics, or hyperparameter tuning. Experiments on Prithvi‑EO‑2, TerraMind, and ImageNet‑pretrained ViT‑MAE show that SIMPLER retains 94% of baseline performance while achieving 2.1× faster training and 2.6× faster inference.