Hyperspectral Image Classification On Indian
Métriques
Overall Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Overall Accuracy |
|---|---|
| attention-based-adaptive-spectral-spatial | 99.57 % |
| jigsawhsi-a-network-for-hyperspectral-image | 99.74 |
| hyperspectral-image-classification-with-deep | 99.50% |
| discrete-cosine-transform-based-joint | - |
| generative-adversarial-networks-based-on-2 | - |
| spectral-spatial-classification-of-2 | - |
| bass-net-band-adaptive-spectral-spatial | 96.77% |
| hyperspectral-image-classification-of | - |
| deep-metric-learning-based-feature-embedding | - |
| deep-supervised-learning-for-hyperspectral | - |
| hyperspectral-image-classification-with-1 | 96.12% |
| spectralnet-exploring-spatial-spectral | 99.86% |
| generative-adversarial-networks-based-on-1 | - |
| small-sample-hyperspectral-image | - |
| hybridsn-exploring-3d-2d-cnn-feature | 99.81% |
| feature-extraction-of-hyperspectral-images | - |
| faster-hyperspectral-image-classification | 99.83% |
| locality-aware-hyperspectral-classification | 89.80 |
| shorten-spatial-spectral-rnn-with-parallel | 90.35% |
| graph-information-aggregation-cross-domain | - |
| exploring-the-relationship-between-center-and | 98.18±0.27% |
| adaptive-mask-sampling-and-manifold-to | 98.38±0.38% |
| attention-based-second-order-pooling-network | 99.24% |
| a-spectral-spatial-dependent-global-learning | 99.63% |
| hyperspectral-image-classification-using-deep | 99.93% |
| hsi-bert-hyperspectral-image-classification | - |
| hyperspectral-image-classification-via-a | - |