Table 14 Detailed classification test results for the Pavia University Dataset in terms of Precision, Recall, and F1-Score. Testing was done with PCA 30 CEU-Net no-patching with a 75%/25% Training/Testing split
From: CEU-Net: ensemble semantic segmentation of hyperspectral images using clustering
Class Labels | Precision | Recall | f1-score | Support |
|---|---|---|---|---|
Asphalt | 0.97 | 0.95 | 0.96 | 1693 |
Meadows | 0.98 | 0.99 | 0.98 | 4629 |
Gravel | 0.89 | 0.84 | 0.86 | 550 |
Trees | 0.99 | 0.95 | 0.97 | 715 |
Painted Metal Sheets | 1.00 | 1.00 | 1.00 | 331 |
Bare Soil | 0.97 | 0.94 | 0.96 | 1313 |
Bitumen | 0.88 | 0.92 | 0.90 | 341 |
Self-Blocking Bricks | 0.88 | 0.92 | 0.90 | 882 |
Shadows | 1.00 | 1.00 | 1.00 | 240 |
Accuracy | Â | Â | 0.96 | 10694 |
Macro Average | 0.95 | 0.95 | 0.95 | 10694 |
Weighted Average | 0.96 | 0.96 | 0.96 | 10694 |