| 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`) | |