Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery

Autores:: Chanchí-Golondrino, Gabriel-Elías
Ospina-Alarcón, Manuel-Alejandro
Saba, Manuel

Tipo de recurso:: Article of journal

Fecha de publicación:: 2024

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

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dc.title.en-US.fl_str_mv	Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery
title	Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery
spellingShingle	Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery Computer vision Fourier analysis Hyperspectral imaging Water bodies detection Remote sensing Machine learning Análisis de Fourier Aprendizaje automático Detección de cuerpos de agua Imágenes hiperespectrales Sensado remoto Visión por computador
title_short	Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery
title_full	Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery
title_fullStr	Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery
title_full_unstemmed	Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery
title_sort	Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery
dc.creator.fl_str_mv	Chanchí-Golondrino, Gabriel-Elías Ospina-Alarcón, Manuel-Alejandro Saba, Manuel
dc.contributor.author.eng.fl_str_mv	Chanchí-Golondrino, Gabriel-Elías Ospina-Alarcón, Manuel-Alejandro Saba, Manuel
dc.subject.none.fl_str_mv	Computer vision Fourier analysis Hyperspectral imaging Water bodies detection Remote sensing Machine learning Análisis de Fourier Aprendizaje automático Detección de cuerpos de agua Imágenes hiperespectrales Sensado remoto Visión por computador
topic	Computer vision Fourier analysis Hyperspectral imaging Water bodies detection Remote sensing Machine learning Análisis de Fourier Aprendizaje automático Detección de cuerpos de agua Imágenes hiperespectrales Sensado remoto Visión por computador
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-03-21 00:00:00 2026-04-16T17:47:29Z
dc.date.available.none.fl_str_mv	2024-03-21 00:00:00
dc.date.issued.none.fl_str_mv	2024-03-21
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dc.relation.references.none.fl_str_mv	Z. A. Lone, A. R. Pais, “Object detection in hyperspectral images,” Digital Signal Processing, vol. 131, e103752, 2022. https://doi.org/10.1016/j.dsp.2022.103752 A. Zahra et al., “Current advances in imaging spectroscopy and its state-of-the-art applications,” Expert Systems with Applications, vol. 238, e122172, 2024. https://doi.org/10.1016/j.eswa.2023.122172 D. R. Green, J. J. Hagon, C. Gómez, B. J. Gregory, “Using Low-Cost UAVs for Environmental Monitoring, Mapping, and Modelling: Examples From the Coastal Zone,” in Coastal Management, 2019, pp. 465-501. https://doi.org/10.1016/B978-0-12-810473-6.00022-4 FlySight S.r.l., “Understanding Hyperspectral Imaging – Benefits, Use Cases & Examples,” 2023. J. Deng et al., “Pixel-level regression for UAV hyperspectral images: Deep learning-based quantitative inverse of wheat stripe rust disease index,” Computers and Electronics in Agriculture, vol. 215, e108434, 2023. https://doi.org/10.1016/j.compag.2023.108434 A. Matese, J. M. Prince Czarnecki, S. Samiappan, R. Moorhead, “Are unmanned aerial vehicle-based hyperspectral imaging and machine learning advancing crop science?,” Trends in Plant Science, vol. 29, no. 2, pp. 196-209, 2024. https://doi.org/10.1016/j.tplants.2023.09.001 N. Noshiri, M. A. Beck, C. P. Bidinosti, C. J. Henry, “A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images,” Smart Agricultural Technology, vol. 5, e100316, 2023. https://doi.org/10.1016/j.atech.2023.100316 N. Li, L. Huo, X. Zhang, “Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images,” Computers and Electronics in Agriculture, vol. 217, e108665, 2024. https://doi.org/10.1016/j.compag.2024.108665 X. Zhao et al., “Evaluating the potential of airborne hyperspectral LiDAR for assessing forest insects and diseases with 3D Radiative Transfer Modeling,” Remote Sensing of Environment, vol. 297, e113759, 2023. https://doi.org/10.1016/j.rse.2023.113759 F. Castillo, L. Arias, H. O. Garcés, “Estimation of temperature, local and global radiation of flames, using retrieved hyperspectral imaging,” Measurement, vol. 208, e112459, 2023. https://doi.org/10.1016/j.measurement.2023.112459 W. Song, Y. Fu, S. Zhao, Y. Zhao, H. Wang, Z. Wang, “Post-fire assessment of heating temperatures experienced by concrete using short video imaging, hyperspectral imaging and laser-induced breakdown spectroscopy,” Construction and Building Materials, vol. 392, e131834, 2023. https://doi.org/10.1016/j.conbuildmat.2023.131834 E. L. Hestir et al., “Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission,” Remote Sensing of Environment, vol. 167, pp. 181-195, 2015. https://doi.org/10.1016/j.rse.2015.05.023 T. Bajjouk et al., “Detection of changes in shallow coral reefs status: Towards a spatial approach using hyperspectral and multispectral data,” Ecological Indicators, vol. 96, pp. 174-191, 2019. https://doi.org/10.1016/j.ecolind.2018.08.052 L. K. Torres Gil, D. Valdelamar Martínez, M. Saba, “The Widespread Use of Remote Sensing in Asbestos, Vegetation, Oil and Gas, and Geology Applications,” Atmosphere (Basel), vol. 14, no. 1, e172, 2023. https://doi.org/10.3390/atmos14010172 R. Gong, J. Wang, X. Wang, Y. Liu, J. Shan, “Analysis of microplastics release from rice package in combination with machine learning and hyperspectral imaging technique,” Food Packaging Shelf Life, vol. 39, e101152, 2023. https://doi.org/10.1016/j.fpsl.2023.101152 B. Graham Ram et al., “Palmer amaranth identification using hyperspectral imaging and machine learning technologies in soybean field,” Computers and Electronics in Agriculture, vol. 215, e108444, 2023. https://doi.org/10.1016/j.compag.2023.108444 J. Xu et al., “Fuzzy graph convolutional network for hyperspectral image classification,” Engineering Applications of Artificial Intelligence, vol. 127, e107280, 2024. https://doi.org/10.1016/j.engappai.2023.107280 H. Xie, J. Lu, J. Han, Y. Zhang, F. Xiong, Z. Zhao, “Fourier coded aperture transform hyperspectral imaging system,” Optics and Lasers in Engineering, vol. 163, e107443, 2023. https://doi.org/10.1016/j.optlaseng.2022.107443 N. M. Francis, F. Pourahmadian, R. A. Lebensohn, R. Dingreville, “A fast Fourier transform-based solver for elastic micropolar composites,” Computer Methods in Applied Mechanics and Engineering, vol. 418, e116510, 2024. https://doi.org/10.1016/j.cma.2023.116510 S. Wang, R. Tian, Q. Zhang, Z. Kang, X. Tang, “The application of trajectory analysis method and Fast Fourier Transform analysis method in the division of flow instability influence regions under ocean conditions,” Progress in Nuclear Energy, vol. 168, e105045, 2024. https://doi.org/10.1016/j.pnucene.2023.105045 R. T. Leon, P. C. Sherrell, A. Šutka, A. V. Ellis, “Decoupling piezoelectric and triboelectric signals from PENGs using the fast fourier transform,” Nano Energy, vol. 110, e108445, 2023. https://doi.org/10.1016/j.nanoen.2023.108445 O. Leitersdorf, Y. Boneh, G. Gazit, R. Ronen, S. Kvatinsky, “FourierPIM: High-throughput in-memory Fast Fourier Transform and polynomial multiplication,” Memories- Materials, Devices, Circuits and Systems, vol. 4, e100034, 2023. https://doi.org/10.1016/j.memori.2023.100034 M. J. Spilsbury, A. Euceda, “Transformada Rápida de Fourier,” Revista de la Escuela de Física, vol. 4, no. 2, pp. 45-52, 2019. https://doi.org/10.5377/ref.v4i2.8276 J. Semmlow, Circuits, Signals and Systems for Bioengineers. Elsevier, 2018. https://doi.org/10.1016/C2015-0-06052-X G. E. Chanchí Golondrino, M. A. Ospina Alarcón, M. Saba, “Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics,” Atmosphere (Basel), vol. 14, no. 7, e1148, 2023. https://doi.org/10.3390/atmos14071148
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dc.relation.ispartofjournal.eng.fl_str_mv	Revista Facultad de Ingeniería
dc.relation.bitstream.none.fl_str_mv	https://revistas.uptc.edu.co/index.php/ingenieria/article/download/17232/13923 https://revistas.uptc.edu.co/index.php/ingenieria/article/download/17232/14306
dc.relation.citationedition.eng.fl_str_mv	Núm. 67 , Año 2024 : January-March 2024
dc.rights.none.fl_str_mv	Gabriel-Elías Chanchí-Golondrino, Manuel-Alejandro Ospina-Alarcón, Manuel Saba - 2023
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spelling	Chanchí-Golondrino, Gabriel-ElíasOspina-Alarcón, Manuel-AlejandroSaba, Manuel2024-03-21 00:00:002026-04-16T17:47:29Z2024-03-21 00:00:002024-03-210121-1129https://repositorio.uptc.edu.co/handle/001/2157210.19053/uptc.01211129.v33.n67.2024.172322357-5328https://doi.org/10.19053/uptc.01211129.v33.n67.2024.17232application/pdftext/xmlUniversidad Pedagógica y Tecnológica de ColombiaGabriel-Elías Chanchí-Golondrino, Manuel-Alejandro Ospina-Alarcón, Manuel Saba - 2023http://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/17232Computer visionFourier analysisHyperspectral imagingWater bodies detectionRemote sensingMachine learningAnálisis de FourierAprendizaje automáticoDetección de cuerpos de aguaImágenes hiperespectralesSensado remotoVisión por computadorFourier Analysis Approach to Identify Water Bodies Through Hyperspectral ImageryArtículo 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_970fb48d4fbd8a85Z. A. Lone, A. R. Pais, “Object detection in hyperspectral images,” Digital Signal Processing, vol. 131, e103752, 2022. https://doi.org/10.1016/j.dsp.2022.103752A. Zahra et al., “Current advances in imaging spectroscopy and its state-of-the-art applications,” Expert Systems with Applications, vol. 238, e122172, 2024. https://doi.org/10.1016/j.eswa.2023.122172D. R. Green, J. J. Hagon, C. Gómez, B. J. Gregory, “Using Low-Cost UAVs for Environmental Monitoring, Mapping, and Modelling: Examples From the Coastal Zone,” in Coastal Management, 2019, pp. 465-501. https://doi.org/10.1016/B978-0-12-810473-6.00022-4FlySight S.r.l., “Understanding Hyperspectral Imaging – Benefits, Use Cases & Examples,” 2023.J. Deng et al., “Pixel-level regression for UAV hyperspectral images: Deep learning-based quantitative inverse of wheat stripe rust disease index,” Computers and Electronics in Agriculture, vol. 215, e108434, 2023. https://doi.org/10.1016/j.compag.2023.108434A. Matese, J. M. Prince Czarnecki, S. Samiappan, R. Moorhead, “Are unmanned aerial vehicle-based hyperspectral imaging and machine learning advancing crop science?,” Trends in Plant Science, vol. 29, no. 2, pp. 196-209, 2024. https://doi.org/10.1016/j.tplants.2023.09.001N. Noshiri, M. A. Beck, C. P. Bidinosti, C. J. Henry, “A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images,” Smart Agricultural Technology, vol. 5, e100316, 2023. https://doi.org/10.1016/j.atech.2023.100316N. Li, L. Huo, X. Zhang, “Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images,” Computers and Electronics in Agriculture, vol. 217, e108665, 2024. https://doi.org/10.1016/j.compag.2024.108665X. Zhao et al., “Evaluating the potential of airborne hyperspectral LiDAR for assessing forest insects and diseases with 3D Radiative Transfer Modeling,” Remote Sensing of Environment, vol. 297, e113759, 2023. https://doi.org/10.1016/j.rse.2023.113759F. Castillo, L. Arias, H. O. Garcés, “Estimation of temperature, local and global radiation of flames, using retrieved hyperspectral imaging,” Measurement, vol. 208, e112459, 2023. https://doi.org/10.1016/j.measurement.2023.112459W. Song, Y. Fu, S. Zhao, Y. Zhao, H. Wang, Z. Wang, “Post-fire assessment of heating temperatures experienced by concrete using short video imaging, hyperspectral imaging and laser-induced breakdown spectroscopy,” Construction and Building Materials, vol. 392, e131834, 2023. https://doi.org/10.1016/j.conbuildmat.2023.131834E. L. Hestir et al., “Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission,” Remote Sensing of Environment, vol. 167, pp. 181-195, 2015. https://doi.org/10.1016/j.rse.2015.05.023T. Bajjouk et al., “Detection of changes in shallow coral reefs status: Towards a spatial approach using hyperspectral and multispectral data,” Ecological Indicators, vol. 96, pp. 174-191, 2019. https://doi.org/10.1016/j.ecolind.2018.08.052L. K. Torres Gil, D. Valdelamar Martínez, M. Saba, “The Widespread Use of Remote Sensing in Asbestos, Vegetation, Oil and Gas, and Geology Applications,” Atmosphere (Basel), vol. 14, no. 1, e172, 2023. https://doi.org/10.3390/atmos14010172R. Gong, J. Wang, X. Wang, Y. Liu, J. Shan, “Analysis of microplastics release from rice package in combination with machine learning and hyperspectral imaging technique,” Food Packaging Shelf Life, vol. 39, e101152, 2023. https://doi.org/10.1016/j.fpsl.2023.101152B. Graham Ram et al., “Palmer amaranth identification using hyperspectral imaging and machine learning technologies in soybean field,” Computers and Electronics in Agriculture, vol. 215, e108444, 2023. https://doi.org/10.1016/j.compag.2023.108444J. Xu et al., “Fuzzy graph convolutional network for hyperspectral image classification,” Engineering Applications of Artificial Intelligence, vol. 127, e107280, 2024. https://doi.org/10.1016/j.engappai.2023.107280H. Xie, J. Lu, J. Han, Y. Zhang, F. Xiong, Z. Zhao, “Fourier coded aperture transform hyperspectral imaging system,” Optics and Lasers in Engineering, vol. 163, e107443, 2023. https://doi.org/10.1016/j.optlaseng.2022.107443N. M. Francis, F. Pourahmadian, R. A. Lebensohn, R. Dingreville, “A fast Fourier transform-based solver for elastic micropolar composites,” Computer Methods in Applied Mechanics and Engineering, vol. 418, e116510, 2024. https://doi.org/10.1016/j.cma.2023.116510S. Wang, R. Tian, Q. Zhang, Z. Kang, X. Tang, “The application of trajectory analysis method and Fast Fourier Transform analysis method in the division of flow instability influence regions under ocean conditions,” Progress in Nuclear Energy, vol. 168, e105045, 2024. https://doi.org/10.1016/j.pnucene.2023.105045R. T. Leon, P. C. Sherrell, A. Šutka, A. V. Ellis, “Decoupling piezoelectric and triboelectric signals from PENGs using the fast fourier transform,” Nano Energy, vol. 110, e108445, 2023. https://doi.org/10.1016/j.nanoen.2023.108445O. Leitersdorf, Y. Boneh, G. Gazit, R. Ronen, S. Kvatinsky, “FourierPIM: High-throughput in-memory Fast Fourier Transform and polynomial multiplication,” Memories- Materials, Devices, Circuits and Systems, vol. 4, e100034, 2023. https://doi.org/10.1016/j.memori.2023.100034M. J. Spilsbury, A. Euceda, “Transformada Rápida de Fourier,” Revista de la Escuela de Física, vol. 4, no. 2, pp. 45-52, 2019. https://doi.org/10.5377/ref.v4i2.8276J. Semmlow, Circuits, Signals and Systems for Bioengineers. Elsevier, 2018. https://doi.org/10.1016/C2015-0-06052-XG. E. Chanchí Golondrino, M. A. Ospina Alarcón, M. Saba, “Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics,” Atmosphere (Basel), vol. 14, no. 7, e1148, 2023. https://doi.org/10.3390/atmos14071148e1723267e1723233Revista Facultad de Ingenieríahttps://revistas.uptc.edu.co/index.php/ingenieria/article/download/17232/13923https://revistas.uptc.edu.co/index.php/ingenieria/article/download/17232/14306Núm. 67 , Año 2024 : January-March 2024PublicationOREORE.xmltext/xml2699https://repositorio.uptc.edu.co/bitstreams/365b1bff-626b-4884-b693-85915d5f05de/download83407c49ca3b6cee5e67ed3eeef81c00MD51falseAnonymousREAD001/21572oai:repositorio.uptc.edu.co:001/215722026-10-29 14:05:04.979http://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery

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