Comparative Assessment of Machine Learning and Neural Network Models for Asbestos–Cement Detection in VNIR Images

Gabriel Elías Chanchí-Golondrino, Isaac Esteban Camargo Freile, Julio Eduardo Mejía Manzano, Manuel Saba, Manuel Alejando Ospina-Alarcón

Hyperspectral imaging is a well-established remote sensing technique for material detection and classification, relying on hundreds of reflectance bands to exploit the spectral signatures of surface materials. Although hyperspectral imagery has demonstrated excellent capabilities for material identification, its operational implementation may be constrained in some applications due to data volume and processing requirements. Consequently, there is growing interest in evaluating the capability of lower-dimensional multispectral imagery for material detection tasks. In this sense, this article proposes as its contribution the comparative evaluation of machine learning models and neural networks for asbestos–cement detection on VNIR imagery. For the development of this research, the CRISP-DM methodology was adapted into four phases: P1. Business and data understanding; P2. Data preparation; P3. Modelling and evaluation; P4. Model deployment. At the results level, three datasets with different numbers of bands were constructed, which were structured by adding to the original dataset an additional layer with the NDVI and two additional layers with the PCA components of the original image. Across the three datasets, four machine learning models and one neural network model were tuned and evaluated, yielding as a result that in all three datasets the KNN and neural network models achieved the best performance. Likewise, it was found that the detection capability of the models improved with the inclusion of the additional bands. The proposed approach serves as a reference to be extrapolated by research centres and universities for the detection of asbestos and other materials in VNIR images, with a view toward integration into resource-constrained systems and specifically into environmental monitoring systems.