DOI:
https://doi.org/10.14483/23448350.23314Publicado:
11/30/2025Número:
Vol. 52 Núm. 2 (2025): Mayo-Agosto 2025Sección:
ArtículosDetection of Asbestos-Cement in Hyperspectral Images Based on the Application of Fourier Phase Similarity
Detección de asbesto-cemento en imágenes hiperespectrales a partir de la aplicación de la similitud de fase de Fourier
Palabras clave:
hyperspectral imaging, asbestos-cement detection, remote sensing, phase similarity, fast Fourier transform (en).Palabras clave:
imágenes hiperespectrales, detección de asbesto-cemento, sensado remoto, similitud de fase, transformada rápida de Fourier (es).Descargas
Resumen (en)
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.
Resumen (es)
Uno de los desafíos en el campo de las imágenes hiperespectrales es la identificación de métodos para la detección eficaz y eficiente de materiales, en vista de la alta dimensionalidad de los datos asociados a los cientos de bandas de reflectancia. En este sentido, dada la regulación en cuanto al uso de asbesto en la construcción y las implicaciones de este material para la salud humana, ha cobrado relevancia la teledetección. En este trabajo se propone un nuevo enfoque computacional para la detección de asbesto-cemento que utiliza la similitud de fase entre la representación espectral de Fourier del pixel característico y la de las demás firmas espectrales en la imagen. Para el desarrollo de esta investigación, se adaptó la metodología CRISP-DM. A manera de resultado, se implementó el enfoque propuesto en una imagen hiperespectral del barrio Manga de la ciudad de Cartagena de Indias (Bolívar, Colombia). El porcentaje de asbesto detectado por medio de nuestro método difiere en un 1.74 % con respecto al tradicional método de correlación. Asimismo, el enfoque propuesto demostró ser 0.86% más eficiente que este último. De acuerdo con los resultados obtenidos, nuestro enfoque se constituye en una alternativa competitiva, siendo de gran utilidad en escenarios que involucren imágenes de gran cobertura y requieran optimizar el tiempo de procesamiento. Dado el uso de tecnologías de código abierto, este enfoque puede ser extrapolado fácilmente en el dominio académico y empresarial con el fin de detectar asbesto-cemento y otros materiales.
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