Method for asbestos detection in hyperspectral images based on the approximate components of the wavelet transform and spectral differential similarity

Gabriel Elías Chanchí Golondrino; Manuel Saba; Manuel Alejandro Ospina Alarcón

doi:10.24054/rcta.v2i46.3521

Authors

Gabriel Elías Chanchí Golondrino Universidad de Cartagena https://orcid.org/0000-0002-0257-1988
Manuel Saba Universidad de Cartagena https://orcid.org/0000-0003-1345-9657
Manuel Alejandro Ospina Alarcón Universidad de Cartagena https://orcid.org/0000-0003-4510-0753

DOI:

https://doi.org/10.24054/rcta.v2i46.3521

Keywords:

Asbestos-cement, hyperspectral images, remote sensing, wavelet transform

Abstract

Considering that one of the challenges in material detection within the field of hyperspectral imaging, given its high dimensionality, is the identification of more computationally efficient methods, this article proposes a method for asbestos detection based on the use of the approximate components of the wavelet transform and spectral differential similarity. Variants of the proposed method were implemented using open-source libraries, including Spectral, PyWavelets, NumPy, Pandas, and Matplotlib, achieving similar effectiveness in asbestos detection compared to the correlation-based method. Furthermore, in terms of computational efficiency, it was found that the three variants of the proposed method were more efficient than the correlation-based method, with the method based on the first component of the wavelet transform yielding the best results, being 13.964% more efficient. Based on these results, the variants of the proposed method can be considered as alternatives to conventional methods, allowing them to be integrated into systems for the analysis and monitoring of asbestos and other materials using hyperspectral images. Additionally, this study demonstrated the feasibility of using open-source tools and libraries for material identification in hyperspectral images, making this research a reference point for research centers and universities to replicate and adapt these methods in remote sensing-based investigations.

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