arXiv Computer Vision
Super-Resolving Unseen Hyperspectral Sensors at Any Scale via Spatial Operators
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arXiv Computer Vision
Sep 7
Learning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution
The paper introduces TSR-ITNR, a two‑stage, self‑supervised framework for hyperspectral image super‑resolution that fuses high‑resolution multispectral and low‑resolution hyperspectral data. Stage 1 refines an implicit Tucker representation using a low‑rank spatial tensor and spectral basis, enhanced by a pretrained denoiser, to capture fine spatial details and spectral correlations. Stage 2 applies parameter‑free calibration to extract complementary corrections from both observations, preserving geometry and ensuring orthogonal complementarity, leading to superior reconstruction quality demonstrated on benchmark datasets and improved downstream segmentation performance.
arXiv Machine Learning
Sep 25
Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision
The paper introduces SCOPE, a method for reconstructing high‑resolution digital elevation models (DEMs) from coarser‑resolution training pairs. SCOPE learns a continuous terrain representation by predicting a latent coefficient field on a low‑resolution grid and reusing local Fourier residual functions, thereby decoupling coefficient prediction from output‑grid construction. Experiments on diverse land–ocean datasets show that SCOPE outperforms competing methods across six metrics, reduces reconstruction error by about 12 % at three‑times‑unseen scale, and achieves these gains with only a modest increase in computational cost.
arXiv AI
Jun 9
Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
arXiv:2606. 08324v1 Announce Type: cross Abstract: Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest.
arXiv Computer Vision
Sep 2
Lightweight Interpretable RGB-Guided Hyperspectral Super-Resolution under Real Cross-resolution Misalignment
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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 Computer Vision
Sep 24
Benchmarking Hyperspectral Foundation Models for Hyperspectral Unmixing
arXiv:2609.28283v1 Announce Type: new Abstract: Several foundation models dedicated to hyperspectral images have recently been made available. These models are trained on large unlabeled datasets and...