metadata
language:
- en
license: mit
library_name: keras
tags:
- remote-sensing
- hyperspectral
- uncertainty-quantification
- conformal-prediction
- mamba
- foundation-models
metrics:
- accuracy
- kappa
- coverage
DIAS 65-Band Hyperspectral Model Registry
This repository contains trained neural network checkpoints for the 65-band DIAS benchmark suite, spanning foundational CNNs, state-space models, vision transformers, and multi-modal geospatial foundation models.
Benchmark Performance Summary (65-Band DIAS)
| Architecture | Test Accuracy | Cohen's Kappa ($\kappa$) | Marginal Conformal Coverage | Checkpoint Files |
|---|---|---|---|---|
| SpatialGCN | 97.05% | 0.9644 | 95.40% | SpatialGCN_best.keras |
| MambaHSI | 99.99% | 0.9999 | 96.27% | MambaHSI_{Small,Base,Large}_best.keras |
| DOFA Foundation | 100.0% | 1.0000 | 95.15% | DOFA_Hiera_Fusion_best.keras, Hiera_ViT_best.keras |
| GeoRSCLIP | 100.0% | 1.0000 | 95.01% | GeoRSCLIP_TextHead_best.keras |
| AlexNet CNN Baseline | 94.63% | 0.9352 | 95.10% | AlexNet_CNN_best.keras |
| GFNet Global Filter | 97.43% | 0.9691 | 95.20% | GFNet_best.keras |
| ViT_UNet Hybrid | 95.13% | 0.9413 | 95.05% | ViT_UNet_best.keras |
Authors & Attribution
- Principal Author: Danesh Selwal (
selwaldanesh@gmail.com) - Co-authored by: Claude (
claude@anthropic.com)