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A Computational Method for Identifying Asbestos-Cement in Hyperspectral Images Using Non-Parametric Gaussian Regression

10.24050/reia.v23i46.1887
Published 2026-09-17
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Abstract

One of the challenges in the field of hyperspectral imaging, due to the high dimensionality of the data, is the need for efficient and computationally effective methods for material detection. As a contribution to this problem, this paper proposes a new computational method for the detection of asbestos-cement, based on modeling its spectral signature using the Gaussian process with an RBF kernel, and comparing a relevant percentage of the modeled curve to the spectral signatures of the image’s pixels using RMSE-based similarity. For this research, a four-phase adaptation of the CRISP-DM methodology was carried out: P1. Business and data understanding; P2. Data preparation; P3. Modeling and evaluation; and P4. Model deployment. As a result, it was found that, in terms of effectiveness, the proposed method is comparable to the correlation method, differing by less than 1% in the percentage of asbestos detected by the correlation. Similarly, in terms of efficiency, the proposed method is on average 7.039% more efficient than the correlation method. In conclusion, the method is a good alternative to conventional methods, such that it can be adapted and integrated into material monitoring systems based on remote sensing and hyperspectral imaging.

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