Comparison of Principal Component Analysis and different band selection methods for classification of construction waste with hyperspectral images |
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| Lennard Wunsch, Gunther Notni |
- Abstract:
- This work presents a machine learning pipeline for construction waste sorting utilizing spectral imaging and comparing different dimensionality reduction methods. Aiming to correctly classify over 90% of objects in our dataset, we applied band selection methods based on Mutual Information, Fisher’s Score, Sequential Forward Selection, and Sequential Backward Selection. In addition, we examined Principal Component Analysis (PCA) and Categorical Maximum Spectral Difference. The performance of each pipeline is evaluated using metrics such as accuracy, precision, recall, F1 Score, and AUC-ROC.
- Download:
- IMEKO-TC2-2025-001.pdf
- DOI:
- 10.21014/tc2-2025.001
- Event details
- IMEKO TC:
- TC2
- Event name:
- IMEKO TC2 PhotoMet 2025
- Title:
2025 IMEKO TC2 International Symposium on Modern Photonic Metrology
PhotoMet 2025 - Shaping the Future of Photonic Metrology
- Place:
- Modena, ITALY
- Time:
- 01 September 2025 - 03 September 2025