Detection of Asbestos-Cement in Hyperspectral Images Based on the Application of Fourier Phase Similarity - Chanchí Golondrino, Gabriel Elias; Ospina-Alarcón, Manuel Alejandro; Saba, Manuel (Revista Científica, 2025)
One of the challenges in the field of hyperspectral imaging is identifying methods for the effective and efficient detection of materials, given the high dimensionality of the data associated with hundreds of reflectance bands. In this regard, given the regulations on the use of asbestos in construction and the implications of this material for human health, remote sensing has become increasingly important. This paper proposes a new computational approach for the detection of asbestos-cement that uses the phase similarity between the Fourier spectral representation of the characteristic pixel and that of the other spectral signatures in the image. The CRISP-DM methodology was adapted for the development of this research. As a result, the proposed approach was implemented on a hyperspectral image of the Manga neighborhood in the city of Cartagena de Indias (Bolívar, Colombia). The percentage of asbestos detected using our method differs by 1.74% from the traditional correlation method. Likewise, the proposed approach proved to be 0.86% more efficient than the latter. Based on the results obtained, our approach is a competitive alternative, being very useful in scenarios involving large-coverage images and requiring optimized processing time. Given the use of open-source technologies, this approach can be easily extrapolated in the academic and business domains to detect asbestos-cement and other materials.