- Research Article
- 10.1109/tgrs.2025.3636112
Case Adaptive Detection Models for Asbestos-Containing Building Materials Based on Hyperspectral Image Processing
- Jan 01, 2025
- IEEE Transactions on Geoscience and Remote Sensing
- Hyeji Sim + 5 more +5
This study introduces case adaptive detection models for detecting asbestos-containing building materials employing short-wave infrared hyperspectral images. The models are optimized for indoor and outdoor conditions, which could be applied to asbestos detection in interior and exterior building materials. Samples were collected from the most probable types and forms of building materials for both asbestos-containing materials (ACMs) and non-asbestos-containing materials (non-ACMs) to represent real-world conditions, utilizing the largest and most diverse dataset to date (11 types, 4 directions, and 4 forms). X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) analyses demonstrated that chrysotile was the main asbestos mineral in the most of ACMs, as evidenced by characteristic chrysotile diffraction peaks, fibrous serpentine morphology, and coincident Mg-Si elemental distributions. The spectral characteristics of building materials are manifested by mineral components for ACMs and non-ACMs, where asbestos absorption (Mg-OH) identifies ACMs. The indoor RF model identified critical absorption features at 2299-2350nm, 1923-1967nm, and 2366-2426nm, corresponding to Mg-OH, OH, and CO₃²⁻ absorptions, achieving 95.3% accuracy. The outdoor RF model incorporated additional Fe-OH and Al-OH bands, achieving 96.5% accuracy. Real-world validation at three asbestos abatement sites and one large-scale site demonstrated practical applicability including scalability and computational efficiency with overall accuracies of 92.1% for indoor and 92.5% for outdoor conditions. Comparative analysis with existing benchmark methodologies confirmed superior performance, with our models significantly outperforming previous approaches. The models successfully detected ACMs across various material conditions, scales and effectively verified complete removal post-abatement with high computational speed. This approach delivers immediate practical value for compliance verification and quality assurance in real-world abatement scenarios and large-scale surveys, providing objective documentation for regulatory compliance while substantially reducing costs compared to conventional destructive sampling methods.
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