Evaluation of the mathematical Simpson's method for asbestos-cement de-tection in hyperspectral images
DOI:
https://doi.org/10.15665/rp.v23i2.3722Abstract
Based on the challenge in hyperspectral images related to identifying effective and efficient methods for material detection, given the high dimensionality associated with spectral bands, this article proposes a novel method for asbestos detection in hyperspectral images. The method is based on the application of Simpson's method to calculate the area under the curve of the asbestos-cement spectral signature and the difference between areas corresponding to pixels of asbestos and other materials. For the development of this research, five methodological phases were defined as follows: F1. Acquisition of sample pixels of asbestos-cement and other materials, F2. Determination of the normalized characteristic pixel of asbestos-cement and its area under the curve, F3. Implementation of the method and identification of detection thresholds using pixels of asbestos and other materials, F4. Deployment of the method on the reference hyperspectral image, F5. Evaluation of the method’s effectiveness and efficiency compared to the correlation method. In terms of results, the proposed method showed greater effectiveness in detecting asbestos-cement within the spectral bands 48 to 157. Similarly, it was found that, computationally, the proposed method is 1.79% more efficient than the correlation method, which is one of the most widely used approaches for material detection in hyperspectral images. Based on these results, the proposed method can serve as a reference for extrapolation to the detection of other materials in these images and can also be integrated into material monitoring systems using hyperspectral images.
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