Vol. 15 No. 88 (2026)
Articles

Non-linear spectral matching for asbestos detection in urban hyperspectral imagery using dynamic time warping

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  • Gabriel Elías Chanchí-Golondrino,
  • Manuel Alejandro Ospina Alarcón,
  • Manuel Saba
Gabriel Elías Chanchí-Golondrino
Universidad de Cartagena, Cartagena de Indias, Colombia.
Author Biography

PhD, Facultad de Ingeniería de la Universidad de Cartagena, Cartagena de Indias, Colombia.

Manuel Alejandro Ospina Alarcón
Universidad de Cartagena, Cartagena de Indias, Colombia.
Author Biography

PhD, Facultad de Ingeniería de la Universidad de Cartagena, Cartagena de Indias, Colombia.

Manuel Saba
Universidad de Cartagena, Cartagena de Indias, Colombia.
Author Biography

PhD, Facultad de Ingeniería de la Universidad de Cartagena, Cartagena de Indias, Colombia.

DOI: https://doi.org/10.34069/AI/2026.88.02.2

Published 2026-10-10

Keywords

  • Dynamic temporal alignment,
  • asbestos,
  • correlation,
  • hyperspectral imaging,
  • remote sensing.

How to Cite

Chanchí-Golondrino, G. E., Ospina Alarcón, M. A., & Saba, M. (2026). Non-linear spectral matching for asbestos detection in urban hyperspectral imagery using dynamic time warping. Amazonia Investiga, 15(88), 29–43. https://doi.org/10.34069/AI/2026.88.02.2

Copyright (c) 2026 Gabriel Elías Chanchí-Golondrino, Manuel Alejandro Ospina Alarcón, Manuel Saba

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Hyperspectral remote sensing has gained importance in applications such as biomedicine, agriculture, and environmental monitoring, where spectral signatures enable material detection using computational methods. This article presents the implementation and evaluation of the Dynamic Time Warping (DTW) method for detecting asbestos in hyperspectral images. The method is compared to the widely used correlation technique. The research follows the CRISP-DM methodology, adapted into four phases: business understanding, data preparation, modeling and evaluation, and deployment. Results show that the DTW method is effective for detecting asbestos in hyperspectral images, though it is less sensitive and less computationally efficient than the correlation method. The study suggests that the DTW method is suitable for identifying materials with non-linear spectral variations and can be applied to detect other materials beyond asbestos. Given the public health relevance of asbestos detection, this method can contribute to more precise identification of asbestos-cement roofs in urban areas. The use of open-source libraries such as Spectral, NumPy, SciPy, and FastDTW for the implementation highlights the accessibility of this approach for academic and industrial applications. This work aims to serve as a reference for future research on the detection of materials using DTW in hyperspectral imaging.

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