Proposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signature

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

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dc.title.spa.fl_str_mv	Proposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signature
title	Proposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signature
spellingShingle	Proposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signature Asbestos Hyperspectral imaging Maximum detection Remote sensing Asbesto Imágenes hiperespectrales Detección de máximos Sensado remoto
title_short	Proposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signature
title_full	Proposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signature
title_fullStr	Proposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signature
title_full_unstemmed	Proposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signature
title_sort	Proposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signature
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 Hyperspectral imaging Maximum detection Remote sensing Asbesto Imágenes hiperespectrales Detección de máximos Sensado remoto
topic	Asbestos Hyperspectral imaging Maximum detection Remote sensing Asbesto Imágenes hiperespectrales Detección de máximos Sensado remoto
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-01-15 00:00:00
dc.date.available.none.fl_str_mv	2025-01-15 00:00:00
dc.date.issued.none.fl_str_mv	2025-01-15
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.identifier.issn.none.fl_str_mv	2027-8306
dc.identifier.doi.none.fl_str_mv	10.19053/uptc.20278306.v15.n1.2025.19183
dc.identifier.eissn.none.fl_str_mv	2389-9417
dc.identifier.url.none.fl_str_mv	https://doi.org/10.19053/uptc.20278306.v15.n1.2025.19183
identifier_str_mv	2027-8306 10.19053/uptc.20278306.v15.n1.2025.19183 2389-9417
url	https://doi.org/10.19053/uptc.20278306.v15.n1.2025.19183
dc.language.iso.eng.fl_str_mv	eng
language	eng
dc.relation.references.eng.fl_str_mv	Abbasi, M., Mostafa, S., Vieira, A. S., Patorniti, N., & Stewart, R. A. (2022). Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review. Sustainability, 14 (13), 8068. https://doi.org/10.3390/su14138068 Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. (2017). Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sensing, 9 (11), 1110. https://doi.org/10.3390/rs9111110 Ai, W., Liu, S., Liao, H., Du, J., Cai, Y., Liao, C., Shi, H., Lin, Y., Junaid, M., Yue, X., & Wang, J. (2022). Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil. Science of The Total Environment, 807, 151030. https://doi.org/10.1016/j.scitotenv.2021.151030 Ang, K. L.-M., & Seng, J. K. P. (2021). Big Data and Machine Learning With Hyperspectral Information in Agriculture. IEEE Access, 9, 36699–36718. https://doi.org/10.1109/ACCESS.2021.3051196 Bodkin, A., Sheinis, A., Norton, A., Daly, J., Beaven, S., & Weinheimer, J. (2009). Snapshot hyperspectral imaging: the hyperpixel array camera. In S. S. Shen & P. E. Lewis (Eds.), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340H. https://doi.org/10.1117/12.818929 Bonifazi, G., Capobianco, G., & Serranti, S. (2015). Hyperspectral imaging applied to the identification and classification of asbestos fibers. 2015 IEEE SENSORS, 1–4. https://doi.org/10.1109/ICSENS.2015.7370458 Bonifazi, G., Capobianco, G., & Serranti, S. (2018). Asbestos containing materials detection and classification by the use of hyperspectral imaging. Journal of Hazardous Materials, 344, 981–993. https://doi.org/10.1016/j.jhazmat.2017.11.056 Bonifazi, G., Capobianco, G., & Serranti, S. (2019). Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste. Applied Sciences, 9 (21), 4587. https://doi.org/10.3390/app9214587 Bonifazi, G., Capobianco, G., Serranti, S., Trotta, O., Bellagamba, S., Malinconico, S., & Paglietti, F. (2024). Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 307, 123672. https://doi.org/10.1016/j.saa.2023.123672 Calvillo, E. A., Mendoza, R., Munoz, J., Martinez, J. C., Vargas, M., & Rodriguez, L. C. (2016). Automatic algorithm to classify and locate research papers using natural language. IEEE Latin America Transactions, 14 (3), 1367–1371. https://doi.org/10.1109/TLA.2016.7459622 Ding, X., Zhang, S., & Li, H. (2016). A Polymorphic Ant Colony Algorithm (PACA) for the Selection of Optimized Band Selection of Hyperspectral Remote Sensing Image. Proceedings of the 2016 International Conference on Engineering and Technology Innovations. https://doi.org/10.2991/iceti-16.2016.37 Eckhard, J., Eckhard, T., Valero, E. M., Nieves, J. L., & Contreras, E. G. (2015). Outdoor scene reflectance measurements using a Bragg-grating-based hyperspectral imager. Applied Optics, 54 (13), D15. https://doi.org/10.1364/AO.54.000D15 Eismann, M. T. (2012). Hyperspectral Remote Sensing. SPIE. https://doi.org/10.1117/3.899758 Espinosa-Zúñiga, J. J. (2020). Aplicación de metodología CRISP-DM para segmentación geográfica de una base de datos pública. Ingeniería, Investigación y Tecnología, 21 (1), 1–13. https://doi.org/10.22201/fi.25940732e.2020.21n1.008 Frassy, F., Candiani, G., Rusmini, M., Maianti, P., Marchesi, A., Nodari, F., Via, G., Albonico, C., & Gianinetto, M. (2014). Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps. Sensors, 14 (9), 15900–15913. https://doi.org/10.3390/s140915900 Gao, L., & Smith, R. T. (2015). Optical hyperspectral imaging in microscopy and spectroscopy - a review of data acquisition. Journal of Biophotonics, 8 (6), 441–456. https://doi.org/10.1002/jbio.201400051 Gao, L., Yao, D., Li, Q., Zhuang, L., Zhang, B., & Bioucas-Dias, J. (2017). A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping. Remote Sensing, 9 (11), 1145. https://doi.org/10.3390/rs9111145 González-Núñez, H., & de la Fuente, R. (2017). Offner imaging spectrometers. Optica Pura y Aplicada, 50 (1), 37–47. https://doi.org/10.7149/OPA.50.1.49506 González, C., Resano, J., Mozos, D., Plaza, A., & Valencia, D. (2010). FPGA Implementation of the Pixel Purity Index Algorithm for Remotely Sensed Hyperspectral Image Analysis. EURASIP Journal on Advances in Signal Processing, 2010 (1), 969806. https://doi.org/10.1155/2010/969806 Gu, Y., Liu, T., Gao, G., Ren, G., Ma, Y., Chanussot, J., & Jia, X. (2021). Multimodal hyperspectral remote sensing: an overview and perspective. Science China Information Sciences, 64 (2), 121301. https://doi.org/10.1007/s11432-020-3084-1 Islam, T., Islam, R., Uddin, P., & Ulhaq, A. (2024). Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery. Remote Sensing, 16 (5), 807. https://doi.org/10.3390/rs16050807 Kaplan, G., Gašparović, M., Kaplan, O., Adjiski, V., Comert, R., & Mobariz, M. A. (2023). Machine Learning-Based Classification of Asbestos-Containing Roofs Using Airborne RGB and Thermal Imagery. Sustainability, 15 (7), 6067. https://doi.org/10.3390/su15076067 Kapre, A., Kunde, S., Mittal, S., & Singhal, R. (2023). RAxC: Reflexivity-based Approximate Computing techniques for efficient remote sensing. 2023 IEEE International Conference on Big Data (BigData), 1168–1173. https://doi.org/10.1109/BigData59044.2023.10386511 Khan, M. J., Khan, H. S., Yousaf, A., Khurshid, K., & Abbas, A. (2018). Modern Trends in Hyperspectral Image Analysis: A Review. IEEE Access, 6, 14118–14129. https://doi.org/10.1109/ACCESS.2018.2812999 Lim, H.-T., & Murukeshan, V. M. (2017). Spatial calibration and image processing requirements of an image fiber bundle based snapshot hyperspectral imaging probe: from raw data to datacube. In A. K. Asundi (Ed.), Fifth International Conference on Optical and Photonics Engineering, 104491P. https://doi.org/10.1117/12.2270739 Lu, B., Dao, P., Liu, J., He, Y., & Shang, J. (2020). Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sensing, 12 (16), 2659. https://doi.org/10.3390/rs12162659 Martinez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernandez-Orallo, J., Kull, M., Lachiche, N., Ramirez-Quintana, M. J., & Flach, P. (2021). CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Transactions on Knowledge and Data Engineering, 33 (8), 3048–3061. https://doi.org/10.1109/TKDE.2019.2962680 Mendes, V. B., Leta, F. R., Conci, A., & Gonçalves, L. B. (2010). Detección de Posición Angular de Embarcaciones, utilizando Técnicas de Visión Computacional y Redes Neurales Artificiales. Información Tecnológica, 21 (6). https://doi.org/10.4067/S0718-07642010000600018 Moroni, M., Lupo, E., Marra, E., & Cenedese, A. (2013). Hyperspectral Image Analysis in Environmental Monitoring: Setup of a New Tunable Filter Platform. Procedia Environmental Sciences, 19, 885–894. https://doi.org/10.1016/j.proenv.2013.06.098 Padhan, P. C., & others. (2012). Application of ARIMA model for forecasting agricultural productivity in India. Journal of Agriculture and Social Sciences, 8 (2), 50–56. Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2019). Estudio Comparativo de Técnicas de Clasificación de Imágenes Hiperespectrales. Revista Iberoamericana de Automática e Informática Industrial, 16 (2), 129. https://doi.org/10.4995/riai.2019.11078 Park, B. (2016). Future Trends in Hyperspectral Imaging. NIR News, 27 (1), 35–38. https://doi.org/10.1255/nirn.1583 Pérez-Roncal, C., López-Maestresalas, A., López-Molina, C., Marín-Ederra, D., Urrestarazu-Vidart, J., Arazuri-Garín, S., Santesteban-García, L. G., & Jarén-Ceballos, C. (2019). Identificación de la presencia de oídio (Erysiphe necator) en racimos de uva mediante imágenes hiperespectrales. Congreso Ibérico de Agroingeniería, 1248–1254. https://doi.org/10.26754/c_agroing.2019.com.3434 Peyghambari, S., & Zhang, Y. (2021). Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review. Journal of Applied Remote Sensing, 15 (03). https://doi.org/10.1117/1.JRS.15.031501 Sahoo, R. (2020). Hyperspectral Remote Sensing. Elsevier. https://doi.org/10.1016/C2018-0-01850-2 Tan, K., Wang, H., Chen, L., Du, Q., Du, P., & Pan, C. (2020). Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. Journal of Hazardous Materials, 382, 120987. https://doi.org/10.1016/j.jhazmat.2019.120987 Valdelamar-Martínez, D., Saba, M., & Torres-Gil, L. K. (2024). Assessment of asbestos-cement roof distribution and prioritized intervention approaches through hyperspectral imaging. Heliyon, 10 (3), e25612. https://doi.org/10.1016/j.heliyon.2024.e25612 Wang, C., Liu, B., Liu, L., Zhu, Y., Hou, J., Liu, P., & Li, X. (2021). A review of deep learning used in the hyperspectral image analysis for agriculture. Artificial Intelligence Review, 54 (7), 5205–5253. https://doi.org/10.1007/s10462-021-10018-y Yi, L., Chen, J. M., Zhang, G., Xu, X., Ming, X., & Guo, W. (2021). Seamless Mosaicking of UAV-Based Push-Broom Hyperspectral Images for Environment Monitoring. Remote Sensing, 13 (22), 4720. https://doi.org/10.3390/rs13224720 Zhang, L., & Zhong, Y. (2010). Analysis of Hyperspectral Remote Sensing Images. In Geospatial Technology for Earth Observation, 235–269. Springer US. https://doi.org/10.1007/978-1-4419-0050-0_9 Zhang, X., Wang, Y., Zhang, N., Xu, D., Luo, H., Chen, B., & Ben, G. (2020). SSDANet: Spectral-Spatial Three-Dimensional Convolutional Neural Network for Hyperspectral Image Classification. IEEE Access, 8, 127167–127180. https://doi.org/10.1109/ACCESS.2020.3008029fgjgh
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spelling	Chanchí Golondrino, Gabriel ElíasOspina Alarcón, Manuel AlejandroSaba, Manuel2025-01-15 00:00:002025-01-15 00:00:002025-01-152027-830610.19053/uptc.20278306.v15.n1.2025.191832389-9417https://doi.org/10.19053/uptc.20278306.v15.n1.2025.19183application/pdftext/xmlengUniversidad Pedagógica y Tecnológica de ColombiaRevista de Investigación, Desarrollo e Innovación - 2025https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/19183AsbestosHyperspectral imagingMaximum detectionRemote sensingAsbestoImágenes hiperespectralesDetección de máximosSensado remotoProposal of a computational method for asbestos detection in hyperspectral images based on the identification of prominent peaks in the spectral signatureArtículo 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_970fb48d4fbd8a85Abbasi, M., Mostafa, S., Vieira, A. S., Patorniti, N., & Stewart, R. A. (2022). Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review. Sustainability, 14 (13), 8068. https://doi.org/10.3390/su14138068Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. (2017). Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sensing, 9 (11), 1110. https://doi.org/10.3390/rs9111110Ai, W., Liu, S., Liao, H., Du, J., Cai, Y., Liao, C., Shi, H., Lin, Y., Junaid, M., Yue, X., & Wang, J. (2022). Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil. Science of The Total Environment, 807, 151030. https://doi.org/10.1016/j.scitotenv.2021.151030Ang, K. L.-M., & Seng, J. K. P. (2021). Big Data and Machine Learning With Hyperspectral Information in Agriculture. IEEE Access, 9, 36699–36718. https://doi.org/10.1109/ACCESS.2021.3051196Bodkin, A., Sheinis, A., Norton, A., Daly, J., Beaven, S., & Weinheimer, J. (2009). Snapshot hyperspectral imaging: the hyperpixel array camera. In S. S. Shen & P. E. Lewis (Eds.), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340H. https://doi.org/10.1117/12.818929Bonifazi, G., Capobianco, G., & Serranti, S. (2015). Hyperspectral imaging applied to the identification and classification of asbestos fibers. 2015 IEEE SENSORS, 1–4. https://doi.org/10.1109/ICSENS.2015.7370458Bonifazi, G., Capobianco, G., & Serranti, S. (2018). Asbestos containing materials detection and classification by the use of hyperspectral imaging. Journal of Hazardous Materials, 344, 981–993. https://doi.org/10.1016/j.jhazmat.2017.11.056Bonifazi, G., Capobianco, G., & Serranti, S. (2019). Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste. Applied Sciences, 9 (21), 4587. https://doi.org/10.3390/app9214587Bonifazi, G., Capobianco, G., Serranti, S., Trotta, O., Bellagamba, S., Malinconico, S., & Paglietti, F. (2024). Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 307, 123672. https://doi.org/10.1016/j.saa.2023.123672Calvillo, E. A., Mendoza, R., Munoz, J., Martinez, J. C., Vargas, M., & Rodriguez, L. C. (2016). Automatic algorithm to classify and locate research papers using natural language. IEEE Latin America Transactions, 14 (3), 1367–1371. https://doi.org/10.1109/TLA.2016.7459622Ding, X., Zhang, S., & Li, H. (2016). A Polymorphic Ant Colony Algorithm (PACA) for the Selection of Optimized Band Selection of Hyperspectral Remote Sensing Image. Proceedings of the 2016 International Conference on Engineering and Technology Innovations. https://doi.org/10.2991/iceti-16.2016.37Eckhard, J., Eckhard, T., Valero, E. M., Nieves, J. L., & Contreras, E. G. (2015). Outdoor scene reflectance measurements using a Bragg-grating-based hyperspectral imager. Applied Optics, 54 (13), D15. https://doi.org/10.1364/AO.54.000D15Eismann, M. T. (2012). Hyperspectral Remote Sensing. SPIE. https://doi.org/10.1117/3.899758Espinosa-Zúñiga, J. J. (2020). Aplicación de metodología CRISP-DM para segmentación geográfica de una base de datos pública. Ingeniería, Investigación y Tecnología, 21 (1), 1–13. https://doi.org/10.22201/fi.25940732e.2020.21n1.008Frassy, F., Candiani, G., Rusmini, M., Maianti, P., Marchesi, A., Nodari, F., Via, G., Albonico, C., & Gianinetto, M. (2014). Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps. Sensors, 14 (9), 15900–15913. https://doi.org/10.3390/s140915900Gao, L., & Smith, R. T. (2015). Optical hyperspectral imaging in microscopy and spectroscopy - a review of data acquisition. Journal of Biophotonics, 8 (6), 441–456. https://doi.org/10.1002/jbio.201400051Gao, L., Yao, D., Li, Q., Zhuang, L., Zhang, B., & Bioucas-Dias, J. (2017). A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping. Remote Sensing, 9 (11), 1145. https://doi.org/10.3390/rs9111145González-Núñez, H., & de la Fuente, R. (2017). Offner imaging spectrometers. Optica Pura y Aplicada, 50 (1), 37–47. https://doi.org/10.7149/OPA.50.1.49506González, C., Resano, J., Mozos, D., Plaza, A., & Valencia, D. (2010). FPGA Implementation of the Pixel Purity Index Algorithm for Remotely Sensed Hyperspectral Image Analysis. EURASIP Journal on Advances in Signal Processing, 2010 (1), 969806. https://doi.org/10.1155/2010/969806Gu, Y., Liu, T., Gao, G., Ren, G., Ma, Y., Chanussot, J., & Jia, X. (2021). Multimodal hyperspectral remote sensing: an overview and perspective. Science China Information Sciences, 64 (2), 121301. https://doi.org/10.1007/s11432-020-3084-1Islam, T., Islam, R., Uddin, P., & Ulhaq, A. (2024). Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery. Remote Sensing, 16 (5), 807. https://doi.org/10.3390/rs16050807Kaplan, G., Gašparović, M., Kaplan, O., Adjiski, V., Comert, R., & Mobariz, M. A. (2023). Machine Learning-Based Classification of Asbestos-Containing Roofs Using Airborne RGB and Thermal Imagery. Sustainability, 15 (7), 6067. https://doi.org/10.3390/su15076067Kapre, A., Kunde, S., Mittal, S., & Singhal, R. (2023). RAxC: Reflexivity-based Approximate Computing techniques for efficient remote sensing. 2023 IEEE International Conference on Big Data (BigData), 1168–1173. https://doi.org/10.1109/BigData59044.2023.10386511Khan, M. J., Khan, H. S., Yousaf, A., Khurshid, K., & Abbas, A. (2018). Modern Trends in Hyperspectral Image Analysis: A Review. IEEE Access, 6, 14118–14129. https://doi.org/10.1109/ACCESS.2018.2812999Lim, H.-T., & Murukeshan, V. M. (2017). Spatial calibration and image processing requirements of an image fiber bundle based snapshot hyperspectral imaging probe: from raw data to datacube. In A. K. Asundi (Ed.), Fifth International Conference on Optical and Photonics Engineering, 104491P. https://doi.org/10.1117/12.2270739Lu, B., Dao, P., Liu, J., He, Y., & Shang, J. (2020). Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. 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IEEE Access, 8, 127167–127180. https://doi.org/10.1109/ACCESS.2020.3008029fgjgh138111915Revista de Investigación, Desarrollo e Innovaciónhttps://revistas.uptc.edu.co/index.php/investigacion_duitama/article/download/19183/15517https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/download/19183/16480Núm. 1 , Año 2025 : Enero-JunioPublicationOREORE.xmltext/xml2884https://repositorio.uptc.edu.co/bitstreams/85f9d073-35ff-439d-93ea-47f75afec8a2/downloadf85758d23acc498a9f11830efaddb489MD51falseAnonymousREAD001/21530oai:repositorio.uptc.edu.co:001/215302026-10-29 18:09:19.546https://creativecommons.org/licenses/by-nc-sa/4.0/metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

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