Research Article
A Two-Stage Optimization Strategy for Fuzzy Object-Based Analysis Using Airborne LiDAR and High-Resolution Orthophotos for Urban Road Extraction
Table 5
Classification assessment (confusion matrix, per class accuracies, and total accuracies) on the study area used for method development.
| User class/sample | Building | Road | Tree | Grass | Bare | Water | Shadow | Sum |
| Confusion matrix |
| Building | 65 | 0 | 0 | 0 | 7 | 0 | 0 | 72 | Road | 2 | 59 | 1 | 8 | 10 | 0 | 1 | 81 | Tree | 1 | 0 | 68 | 2 | 0 | 0 | 1 | 72 | Grass | 0 | 0 | 1 | 23 | 5 | 1 | 1 | 31 | Bare | 7 | 7 | 0 | 2 | 66 | 0 | 0 | 82 | Water | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 4 | Shadow | 0 | 0 | 5 | 1 | 0 | 0 | 24 | 30 | Sum | 76 | 66 | 75 | 36 | 88 | 5 | 27 | |
| Accuracy |
| Producer | 0.85 | 0.89 | 0.90 | 0.63 | 0.75 | 0.80 | 0.88 | | User | 0.90 | 0.72 | 0.94 | 0.74 | 0.80 | 1 | 0.80 | | KIA per class | 0.82 | 0.86 | 0.88 | 0.60 | 0.68 | 0.80 | 0.88 | |
| Overall accuracy (%) | 82 | Kappa index of agreement (KIA) | 0.79 |
|
|