Research map / Probabilistic and scientific modeling / Variational and generative sampling
LREN: Low-Rank Embedded Network for Sample-Free Hyperspectral Anomaly Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Remove the ℓ1,2-norm penalty on the anomaly (ablation) | LREN · Four-dataset average | AUC (Pd, Pf): 0.9951 -> 0.8692 | Hurt |
| Replace the density estimation network with Kmeans (ablation) | LREN · Four-dataset average | AUC (Pd, Pf): 0.9951 -> 0.7677 | Hurt |
| Search the lowest-rank representation in the original space instead of the latent space (ablation) | LREN · Four-dataset average | AUC (Pd, Pf): 0.9951 -> 0.9843 | Hurt |
| Learn spectral mapping and dictionary construction separately instead of jointly (ablation) | LREN · Four-dataset average | AUC (Pd, Pf): 0.9951 -> 0.9428 | Hurt |
| Use LREN rather than the strongest listed baseline | Hyperspectral anomaly detection · SanDiego, Hydice, Coast, and Pavia | AUC (Pd, Pf): SanDiego: 0.9894 -> 0.9897; Hydice: 0.9983 -> 0.9998; Coast: 0.9946 -> 0.9982; Pavia: 0.9887 -> 0.9925 | Helped |
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