Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance

Autores:
Chanchí Golondrino, Gabriel Elías
Ospina Alarcón, Manuel Alejandro
Saba, Manuel
Tipo de recurso:
Article of journal
Fecha de publicación:
2025
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
eng
OAI Identifier:
oai:repositorio.uptc.edu.co:001/18806
Acceso en línea:
https://doi.org/10.19053/uptc.01217488.v16.n2.2025.18900
Palabra clave:
Asbestos detection
Hamming distance
hyperspectral images
remote sensing
Detección de asbesto
distancia de Hamming
imágenes hiperespectrales
sensado remoto
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
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network_name_str RiUPTC: Repositorio Institucional UPTC
repository_id_str
dc.title.es-ES.fl_str_mv Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance
title Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance
spellingShingle Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance
Asbestos detection
Hamming distance
hyperspectral images
remote sensing
Detección de asbesto
distancia de Hamming
imágenes hiperespectrales
sensado remoto
title_short Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance
title_full Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance
title_fullStr Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance
title_full_unstemmed Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance
title_sort Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance
dc.creator.fl_str_mv Chanchí Golondrino, Gabriel Elías
Ospina Alarcón, Manuel Alejandro
Saba, Manuel
dc.contributor.author.spa.fl_str_mv Chanchí Golondrino, Gabriel Elías
Ospina Alarcón, Manuel Alejandro
Saba, Manuel
dc.subject.none.fl_str_mv Asbestos detection
Hamming distance
hyperspectral images
remote sensing
Detección de asbesto
distancia de Hamming
imágenes hiperespectrales
sensado remoto
topic Asbestos detection
Hamming distance
hyperspectral images
remote sensing
Detección de asbesto
distancia de Hamming
imágenes hiperespectrales
sensado remoto
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-07-25 00:00:00
dc.date.available.none.fl_str_mv 2025-07-25 00:00:00
dc.date.issued.none.fl_str_mv 2025-07-25
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.local.eng.fl_str_mv Journal article
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dc.type.content.eng.fl_str_mv Text
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dc.identifier.issn.none.fl_str_mv 0121-7488
dc.identifier.doi.none.fl_str_mv 10.19053/uptc.01217488.v16.n2.2025.18900
dc.identifier.eissn.none.fl_str_mv 2462-7658
dc.identifier.url.none.fl_str_mv https://doi.org/10.19053/uptc.01217488.v16.n2.2025.18900
identifier_str_mv 0121-7488
10.19053/uptc.01217488.v16.n2.2025.18900
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url https://doi.org/10.19053/uptc.01217488.v16.n2.2025.18900
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.references.eng.fl_str_mv J. Awange and J. Kiema, “Fundamentals of Remote Sensing,” in Environmental Geoinformatics, 2019, pp. 115–123. doi: 10.1007/978-3-030-03017-9_7. [2] R. Navalgund, V. Jayaraman, and P. S. Roy, “Remote sensing applications : An overview,” Curr. Sci., vol. 93, pp. 1747–1766, 2007. [3] C. Nansen and N. Elliott, “Remote Sensing and Reflectance Profiling in Entomology,” Annu. Rev. Entomol., vol. 61, no. 1, pp. 139–158, Mar. 2016, doi: 10.1146/annurev-ento-010715-023834. [4] A. F. Jiménez-López, M. Jiménez-López, and F. R. Jiménez-López, “Multispectral analysis of vegetation for remote sensing applications,” ITECKNE, vol. 12, no. 2, Nov. 2015, doi: 10.15332/iteckne.v12i2.1242. [5] A. A. Misra, “Remote Sensing Fundamentals,” in Atlas of Structural Geological and Geomorphological Interpretation of Remote Sensing Images, Wiley, 2022, pp. 7–14. doi: 10.1002/9781119813392.ch1. [6] L. Gao and R. T. Smith, “Optical hyperspectral imaging in microscopy and spectroscopy - a review of data acquisition,” J. Biophotonics, vol. 8, no. 6, pp. 441–456, Jun. 2015, doi: 10.1002/jbio.201400051. [7] J. Burger and A. Gowen, “Data handling in hyperspectral image analysis,” Chemom. Intell. Lab. Syst., vol. 108, no. 1, pp. 13–22, Aug. 2011, doi: 10.1016/j.chemolab.2011.04.001. [8] H.-T. Lim and V. M. Murukeshan, “Spatial calibration and image processing requirements of an image fiber bundle based snapshot hyperspectral imaging probe: from raw data to datacube,” A. K. Asundi, Ed., Jun. 2017, p. 104491P. doi: 10.1117/12.2270739. [9] R. Pizzolante and B. Carpentieri, “Hyperspectral Data: Efficient and Secure Transmission,” Algorithms, vol. 10, no. 4, p. 132, Nov. 2017, doi: 10.3390/a10040132. [10] M. J. Khan, H. S. Khan, A. Yousaf, K. Khurshid, and A. Abbas, “Modern Trends in Hyperspectral Image Analysis: A Review,” IEEE Access, vol. 6, pp. 14118–14129, 2018, doi: 10.1109/ACCESS.2018.2812999. [11] Abhinav, A. J. S, and S. Singh, “Spectral Signature Analysis of Land Sat TM Images for Improved Hyper Spectral Image Analysis,” in 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC), IEEE, Jan. 2024, pp. 1–6. doi: 10.1109/ICOCWC60930.2024.10470851. [12] G. Bonifazi, G. Capobianco, and S. Serranti, “Asbestos containing materials detection and classification by the use of hyperspectral imaging,” J. Hazard. Mater., vol. 344, pp. 981–993, Feb. 2018, doi: 10.1016/j.jhazmat.2017.11.056. [13] G. Bonifazi, G. Capobianco, and S. Serranti, “Hyperspectral imaging applied to the identification and classification of asbestos fibers,” in 2015 IEEE SENSORS, IEEE, Nov. 2015, pp. 1–4. doi: 10.1109/ICSENS.2015.7370458. [14] G. Bonifazi, G. Capobianco, and S. Serranti, “Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste,” Appl. Sci., vol. 9, no. 21, p. 4587, Oct. 2019, doi: 10.3390/app9214587. [15] O. Trotta, G. Bonifazi, G. Capobianco, and S. Serranti, “DETECTION OF ASBESTOS CONTAINING MATERIAL IN POST-EARTHQUAKE BUILDING WASTE THROUGH HYPERSPECTRAL IMAGING AND MICRO-X-RAY FLUORESCENCE,” Detritus, no. 21, pp. 27–34, Dec. 2022, doi: 10.31025/2611-4135/2022.17233. [16] Y. Jeong, J. Yu, L. Wang, H. H. Huynh, and H.-C. Kim, “Monitoring Asbestos Mine Remediation Using Airborne Hyperspectral Imaging System: A Case Study of Jefferson Lake Mine, US,” Remote Sens., vol. 14, no. 21, p. 5572, Nov. 2022, doi: 10.3390/rs14215572. [17] W. Dong, C. Zhou, F. Wu, J. Wu, G. Shi, and X. Li, “Model-Guided Deep Hyperspectral Image Super-Resolution,” IEEE Trans. Image Process., vol. 30, pp. 5754–5768, 2021, doi: 10.1109/TIP.2021.3078058. [18] B. Mohan and A. Porwal, “Hyperspectral Image Processing and Analysis,” Curr. Sci., vol. 108, pp. 833–841, 2015, doi: https://doi.org/10.18520/CS/V108/I5/833-841. [19] M. E. Paoletti, J. M. Haut, N. S. Pereira, J. Plaza, and A. Plaza, “Ghostnet for Hyperspectral Image Classification,” IEEE Trans. Geosci. Remote Sens., vol. 59, no. 12, pp. 10378–10393, Dec. 2021, doi: 10.1109/TGRS.2021.3050257. [20] S. M. Schweizer and J. M. F. Moura, “Efficient detection in hyperspectral imagery,” IEEE Trans. Image Process., vol. 10, no. 4, pp. 584–597, Apr. 2001, doi: 10.1109/83.913593. [21] R. Guerra, S. Lopez, and R. Sarmiento, “A Computationally Efficient Algorithm for Fusing Multispectral and Hyperspectral Images,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 10, pp. 5712–5728, Oct. 2016, doi: 10.1109/TGRS.2016.2570433. [22] A. Soccio, J. P. Barbosa, and M. S. Reis, “A scalable approach for the efficient segmentation of hyperspectral images,” Chemom. Intell. Lab. Syst., vol. 213, p. 104314, Jun. 2021, doi: 10.1016/j.chemolab.2021.104314. [23] G. E. Chanchí Golondrino, M. A. Ospina Alarcón, and M. Saba, “Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics,” Atmosphere (Basel)., vol. 14, no. 7, p. 1148, Jul. 2023, doi: 10.3390/atmos14071148. [24] E. F. AVILA VELEZ, “Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de estudio rio Cuja Cundinamarca-Colombia,” Cienc. en Desarro., vol. 15, no. 2, Oct. 2024, doi: 10.19053/uptc.01217488.v15.n2.2024.16499. [25] J. D. Caceres and A. N. Venkata, “Asbestos-associated pulmonary disease,” Curr. Opin. Pulm. Med., vol. 29, no. 2, pp. 76–82, Mar. 2023, doi: 10.1097/MCP.0000000000000939. [26] S. V. Jargin, “Asbestos-related Lung Diseases: A Brief Update,” J. Inflamm. Dis., vol. 26, no. 2, pp. 107–114, Jul. 2022, doi: 10.32598/JID.26.2.2. [27] S. Klebe, V. Rathi, and P. A. Russell, “Lung cancer caused by asbestos: What a reporting pathologist needs to know,” Lung Cancer, vol. 195, p. 107849, Sep. 2024, doi: 10.1016/j.lungcan.2024.107849. [28] S. J. Hussein and Z. Fakhri Merzah, “Analysis of Hyperspectral Remote Sensing Images for Extraction Geological Rock Types Maps by Geospatial Techniques,” IOP Conf. Ser. Mater. Sci. Eng., vol. 901, no. 1, p. 012016, Aug. 2020, doi: 10.1088/1757-899X/901/1/012016. [29] H. S. Jaber, “Classification of Hyperspectral Remote Sensing for Production Minerals Mapping Using Geological Map and Geomatics Techniques,” Int. J. Eng. Technol., 2018, doi: 10.14419/IJET.V7I4.20.26247. [30] B. Bernacki and M. Phillips, “Standoff hyperspectral imaging of explosives residues using broadly tunable external cavity quantum cascade laser illumination,” in SPIE Defense, Security, and Sensing, 2010. doi: 10.1117/12.849543.
A. Korchevskiy and A. G. Wylie, “Asbestos Exposure, Lung Fiber Burden, and Mesothelioma Rates: Mechanistic Modelling for Risk Assessment,” Computational Toxicology, vol. 24, p. 100249, 2022. doi:10.1016/j.comtox.2022.100249.
J. M. Ramada Rodilla, B. Calvo Cerrada, C. Serra Pujadas, G. L. Delclos, and F. G. Benavides, “Fiber burden and asbestos-related diseases: an umbrella review,” Gaceta Sanitaria, vol. 36, no. 2, pp. 173–183, 2022. doi: 10.1016/j.gaceta.2021.04.001.
P. V. Panou, M. Vyberg, C. Meristoudis, J. Hansen, M. Bøgsted, Ø. Omland, U. M. Weinreich, and O. D. Røe, “Non-occupational exposure to asbestos is the main cause of malignant mesothelioma in women in North Jutland, Denmark,” Scandinavian Journal of Work, Environment & Health, vol. 45, no. 1, pp. 82–89, 2019. doi: 10.5271/sjweh.3756.
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spelling Chanchí Golondrino, Gabriel ElíasOspina Alarcón, Manuel AlejandroSaba, Manuel2025-07-25 00:00:002025-07-25 00:00:002025-07-250121-748810.19053/uptc.01217488.v16.n2.2025.189002462-7658https://doi.org/10.19053/uptc.01217488.v16.n2.2025.18900application/pdfengUniversidad Pedagógica y Tecnológica de Colombiahttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esinfo:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/18900Asbestos detectionHamming distancehyperspectral imagesremote sensingDetección de asbestodistancia de Hammingimágenes hiperespectralessensado remotoProposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distanceArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85J. Awange and J. Kiema, “Fundamentals of Remote Sensing,” in Environmental Geoinformatics, 2019, pp. 115–123. doi: 10.1007/978-3-030-03017-9_7. [2] R. Navalgund, V. Jayaraman, and P. S. Roy, “Remote sensing applications : An overview,” Curr. Sci., vol. 93, pp. 1747–1766, 2007. [3] C. Nansen and N. Elliott, “Remote Sensing and Reflectance Profiling in Entomology,” Annu. Rev. Entomol., vol. 61, no. 1, pp. 139–158, Mar. 2016, doi: 10.1146/annurev-ento-010715-023834. [4] A. F. Jiménez-López, M. Jiménez-López, and F. R. Jiménez-López, “Multispectral analysis of vegetation for remote sensing applications,” ITECKNE, vol. 12, no. 2, Nov. 2015, doi: 10.15332/iteckne.v12i2.1242. [5] A. A. Misra, “Remote Sensing Fundamentals,” in Atlas of Structural Geological and Geomorphological Interpretation of Remote Sensing Images, Wiley, 2022, pp. 7–14. doi: 10.1002/9781119813392.ch1. [6] L. Gao and R. T. Smith, “Optical hyperspectral imaging in microscopy and spectroscopy - a review of data acquisition,” J. Biophotonics, vol. 8, no. 6, pp. 441–456, Jun. 2015, doi: 10.1002/jbio.201400051. [7] J. Burger and A. Gowen, “Data handling in hyperspectral image analysis,” Chemom. Intell. Lab. Syst., vol. 108, no. 1, pp. 13–22, Aug. 2011, doi: 10.1016/j.chemolab.2011.04.001. [8] H.-T. Lim and V. M. Murukeshan, “Spatial calibration and image processing requirements of an image fiber bundle based snapshot hyperspectral imaging probe: from raw data to datacube,” A. K. Asundi, Ed., Jun. 2017, p. 104491P. doi: 10.1117/12.2270739. [9] R. Pizzolante and B. Carpentieri, “Hyperspectral Data: Efficient and Secure Transmission,” Algorithms, vol. 10, no. 4, p. 132, Nov. 2017, doi: 10.3390/a10040132. [10] M. J. Khan, H. S. Khan, A. Yousaf, K. Khurshid, and A. Abbas, “Modern Trends in Hyperspectral Image Analysis: A Review,” IEEE Access, vol. 6, pp. 14118–14129, 2018, doi: 10.1109/ACCESS.2018.2812999. [11] Abhinav, A. J. S, and S. Singh, “Spectral Signature Analysis of Land Sat TM Images for Improved Hyper Spectral Image Analysis,” in 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC), IEEE, Jan. 2024, pp. 1–6. doi: 10.1109/ICOCWC60930.2024.10470851. [12] G. Bonifazi, G. Capobianco, and S. Serranti, “Asbestos containing materials detection and classification by the use of hyperspectral imaging,” J. Hazard. Mater., vol. 344, pp. 981–993, Feb. 2018, doi: 10.1016/j.jhazmat.2017.11.056. [13] G. Bonifazi, G. Capobianco, and S. Serranti, “Hyperspectral imaging applied to the identification and classification of asbestos fibers,” in 2015 IEEE SENSORS, IEEE, Nov. 2015, pp. 1–4. doi: 10.1109/ICSENS.2015.7370458. [14] G. Bonifazi, G. Capobianco, and S. Serranti, “Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste,” Appl. Sci., vol. 9, no. 21, p. 4587, Oct. 2019, doi: 10.3390/app9214587. [15] O. Trotta, G. Bonifazi, G. Capobianco, and S. Serranti, “DETECTION OF ASBESTOS CONTAINING MATERIAL IN POST-EARTHQUAKE BUILDING WASTE THROUGH HYPERSPECTRAL IMAGING AND MICRO-X-RAY FLUORESCENCE,” Detritus, no. 21, pp. 27–34, Dec. 2022, doi: 10.31025/2611-4135/2022.17233. [16] Y. Jeong, J. Yu, L. Wang, H. H. Huynh, and H.-C. Kim, “Monitoring Asbestos Mine Remediation Using Airborne Hyperspectral Imaging System: A Case Study of Jefferson Lake Mine, US,” Remote Sens., vol. 14, no. 21, p. 5572, Nov. 2022, doi: 10.3390/rs14215572. [17] W. Dong, C. Zhou, F. Wu, J. Wu, G. Shi, and X. Li, “Model-Guided Deep Hyperspectral Image Super-Resolution,” IEEE Trans. Image Process., vol. 30, pp. 5754–5768, 2021, doi: 10.1109/TIP.2021.3078058. [18] B. Mohan and A. Porwal, “Hyperspectral Image Processing and Analysis,” Curr. Sci., vol. 108, pp. 833–841, 2015, doi: https://doi.org/10.18520/CS/V108/I5/833-841. [19] M. E. Paoletti, J. M. Haut, N. S. Pereira, J. Plaza, and A. Plaza, “Ghostnet for Hyperspectral Image Classification,” IEEE Trans. Geosci. Remote Sens., vol. 59, no. 12, pp. 10378–10393, Dec. 2021, doi: 10.1109/TGRS.2021.3050257. [20] S. M. Schweizer and J. M. F. Moura, “Efficient detection in hyperspectral imagery,” IEEE Trans. Image Process., vol. 10, no. 4, pp. 584–597, Apr. 2001, doi: 10.1109/83.913593. [21] R. Guerra, S. Lopez, and R. Sarmiento, “A Computationally Efficient Algorithm for Fusing Multispectral and Hyperspectral Images,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 10, pp. 5712–5728, Oct. 2016, doi: 10.1109/TGRS.2016.2570433. [22] A. Soccio, J. P. Barbosa, and M. S. Reis, “A scalable approach for the efficient segmentation of hyperspectral images,” Chemom. Intell. Lab. Syst., vol. 213, p. 104314, Jun. 2021, doi: 10.1016/j.chemolab.2021.104314. [23] G. E. Chanchí Golondrino, M. A. Ospina Alarcón, and M. Saba, “Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics,” Atmosphere (Basel)., vol. 14, no. 7, p. 1148, Jul. 2023, doi: 10.3390/atmos14071148. [24] E. F. AVILA VELEZ, “Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de estudio rio Cuja Cundinamarca-Colombia,” Cienc. en Desarro., vol. 15, no. 2, Oct. 2024, doi: 10.19053/uptc.01217488.v15.n2.2024.16499. [25] J. D. Caceres and A. N. Venkata, “Asbestos-associated pulmonary disease,” Curr. Opin. Pulm. Med., vol. 29, no. 2, pp. 76–82, Mar. 2023, doi: 10.1097/MCP.0000000000000939. [26] S. V. Jargin, “Asbestos-related Lung Diseases: A Brief Update,” J. Inflamm. Dis., vol. 26, no. 2, pp. 107–114, Jul. 2022, doi: 10.32598/JID.26.2.2. [27] S. Klebe, V. Rathi, and P. A. Russell, “Lung cancer caused by asbestos: What a reporting pathologist needs to know,” Lung Cancer, vol. 195, p. 107849, Sep. 2024, doi: 10.1016/j.lungcan.2024.107849. [28] S. J. Hussein and Z. Fakhri Merzah, “Analysis of Hyperspectral Remote Sensing Images for Extraction Geological Rock Types Maps by Geospatial Techniques,” IOP Conf. Ser. Mater. Sci. Eng., vol. 901, no. 1, p. 012016, Aug. 2020, doi: 10.1088/1757-899X/901/1/012016. [29] H. S. Jaber, “Classification of Hyperspectral Remote Sensing for Production Minerals Mapping Using Geological Map and Geomatics Techniques,” Int. J. Eng. Technol., 2018, doi: 10.14419/IJET.V7I4.20.26247. [30] B. Bernacki and M. Phillips, “Standoff hyperspectral imaging of explosives residues using broadly tunable external cavity quantum cascade laser illumination,” in SPIE Defense, Security, and Sensing, 2010. doi: 10.1117/12.849543.A. Korchevskiy and A. G. Wylie, “Asbestos Exposure, Lung Fiber Burden, and Mesothelioma Rates: Mechanistic Modelling for Risk Assessment,” Computational Toxicology, vol. 24, p. 100249, 2022. doi:10.1016/j.comtox.2022.100249.J. M. Ramada Rodilla, B. Calvo Cerrada, C. Serra Pujadas, G. L. Delclos, and F. G. Benavides, “Fiber burden and asbestos-related diseases: an umbrella review,” Gaceta Sanitaria, vol. 36, no. 2, pp. 173–183, 2022. doi: 10.1016/j.gaceta.2021.04.001.P. V. Panou, M. Vyberg, C. Meristoudis, J. Hansen, M. Bøgsted, Ø. Omland, U. M. Weinreich, and O. D. 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