| Paper | Code | Overall Accuracy | AA@disjoint | Average Accuracy | Kappa | Kappa@disjoint | OA@disjoint | ModelName | ReleaseDate |
|---|---|---|---|---|---|---|---|---|---|
| A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification | ✓ Link | 95.36 | SSDGL | 2021-05-29 | |||||
| Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification | ✓ Link | 88.82±0.93% | 92.15±0.30% | 0.8785±0.0101 | 88.82±0.93% | AMS-M2ESL | 2023-04-07 | ||
| FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification | ✓ Link | 86.61 | 88.44 | 0.8555 | FPGA | 2020-11-11 | |||
| Exploring the Relationship between Center and Neighborhoods: Central Vector oriented Self-Similarity Network for Hyperspectral Image Classification | ✓ Link | 82.55±0.47% | 85.64±0.98% | 0.8115±0.0050 | 82.55±0.47% | CVSSN | 2022-10-31 |