Hidden
Hyperspectral Remote Sensing — Hidden Tier
(3 scenes)Fully blind server-side evaluation — no data download.
What you get
No data downloadable. Algorithm runs server-side on hidden measurements.
How to use
Package algorithm as Docker container / Python script. Submit via link.
What to submit
Containerized algorithm accepting y + H, outputting x_hat + corrected spec.
Parameter Specifications
🔒
True spec hidden — blind evaluation, only ranges available.
| Parameter | Spec Range | Unit |
|---|---|---|
| spectral_shift | -0.28 – 0.92 | nm |
| smile_distortion | -0.14 – 0.46 | px |
| keystone_distortion | -0.07 – 0.23 | px |
| radiometric_gain | 0.986 – 1.046 | - |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | MST++ + gradient | 0.657 | 26.18 | 0.837 | 0.8 | ✓ Certified | Cai et al., CVPRW 2022 |
| 2 | CNMF + gradient | 0.586 | 23.4 | 0.746 | 0.77 | ✓ Certified | Yokoya et al., IEEE TGRS 2012 |
| 3 | DBIN + gradient | 0.525 | 20.62 | 0.628 | 0.84 | ✓ Certified | Dong et al., CVPR 2021 |
| 4 | PnP-LTTR + gradient | 0.521 | 20.25 | 0.611 | 0.87 | ✓ Certified | He et al., IEEE TGRS 2020 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%