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

Resumen

Este estudio propone un método computacional para detectar asbesto en imágenes hiperespectrales. La metodología incluye cinco fases: selección de píxeles de muestra y obtención del píxel característico, determinación de los picos prominentes de la curva espectral, implementación del método con definición de umbrales de referencia, aplicación del método a imágenes de prueba, y evaluación comparativa de efectividad y eficiencia. El método identifica píxeles de asbesto calculando la distancia euclidiana entre los picos prominentes de las curvas espectrales. Los resultados muestran que no hay traslape entre las distancias máximas de píxeles de asbesto y las mínimas de no asbesto, logrando detectar el 11.87% de píxeles de asbesto en la imagen de prueba. Aunque el método de correlación es 1.02% más rápido, la diferencia es mínima. Este método puede ser aplicado a otros materiales y contribuye al diagnóstico urbano de materiales peligrosos como el asbesto.

Palabras clave

asbesto, imágenes hiperespectrales, detección de máximos, sensado remoto

PDF (English)

Biografía del autor/a

Gabriel Elías Chanchí-Golondrino

Ingeniero en Electrónica y Telecomunicaciones de la Universidad del Cauca, Doctor en Ingeniería Telemática de la Universidad del Cauca.

Manuel Alejandro Ospina-Alarcón

Ingeniero de Control de la Universidad Nacional de Colombia, Doctor en Ingeniería - Ciencia y Tecnología de Materiales de la Universidad Nacional de Colombia.

Manuel Saba

Ingeniero Civil de la Universidad de Cagliari - Italia, Doctor en Ingeniería con énfasis en Ciencia y Tecnología de Materiales de la Universidad de Cartagena - Colombia.


Citas

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. Eismann, M. T. (2012). Hyperspectral Remote Sensing. SPIE. https://doi.org/10.1117/3.899758
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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.
  31. 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
  32. Park, B. (2016). Future Trends in Hyperspectral Imaging. NIR News, 27 (1), 35–38. https://doi.org/10.1255/nirn.1583
  33. 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
  34. 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
  35. Sahoo, R. (2020). Hyperspectral Remote Sensing. Elsevier. https://doi.org/10.1016/C2018-0-01850-2
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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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