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Introduction: hyperspectral images, unlike conventional images, are composed of numerous channels that provide detailed information about the spectral signatures of objects. This allows for the identification of the materials that make them up, and given their potential for detecting environmental changes, identifying vegetation in urban settings using effective computational methods becomes relevant.
Objective: the objective of this research is to propose a computational method based on Fourier analysis for detecting vegetation in hyperspectral images.
Methods: the research was developed in four methodological phases: selection of technologies, acquisition of the characteristic vegetation pixel, determination of phase similarity between the characteristic pixel and vegetation and non-vegetation pixels, validation of the method on a test hyperspectral image. A method was implemented using the spectral and numpy libraries in Python.
Results: the Fourier analysis yielded an average phase similarity of 89.89% and a minimum similarity of 64.54% between the characteristic vegetation pixel and 100 training vegetation pixels. For non-vegetation pixels, the average phase similarity was 42.19%, with a maximum similarity of 63.98%. These results indicate that the proposed method successfully differentiates between vegetation and non-vegetation pixels.
Conclusion: the results demonstrate that the Fourier-based method can accurately identify vegetation areas in hyperspectral images, showing non-overlapping phase similarities between vegetation and non-vegetation. This validates the effectiveness of the proposed approach in detecting vegetation in urban environments.

Gabriel E. Chanchí-Golondrino, Universidad de Cartagena, Facultad de Ingeniería, Cartagena de Indias, Colombia

Facultad de Ingeniería de la Universidad de Cartagena

Manuel A. Ospina-Alarcón, Universidad de Cartagena, Facultad de Ingeniería, Cartagena de Indias, Colombia

Facultad de Ingeniería de la Universidad de Cartagena

1.
Chanchí-Golondrino GE, Ospina-Alarcón MA, Saba M. Fourier analysis for detecting vegetation in hyperspectral images. inycomp [Internet]. 2024 Oct. 8 [cited 2024 Nov. 7];26(3):e-21013493. Available from: https://revistaingenieria.univalle.edu.co/index.php/ingenieria_y_competitividad/article/view/13493

León-Pérez J. Imágenes hiperespectrales y sus aplicaciones en estudios de suelos, cultivos y bosques, en la era de la cuarta revolución industrial. Rev UD y la Geomática. 2021;(16):40–70.

Erturk A, Cesmeci D, Gullu MK, Gercek D, Erturk S. Endmember Extraction Guided by Anomalies and Homogeneous Regions for Hyperspectral Images. IEEE J Sel Top Appl Earth Obs Remote Sens [Internet]. 2014 Aug;7(8):3630–9. Available from: https://ieeexplore.ieee.org/document/6847728/ DOI: https://doi.org/10.1109/JSTARS.2014.2330364

Richards JA. Remote Sensing Digital Image Analysis [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013. Available from: https://link.springer.com/10.1007/978-3-642-30062-2

Camacho-Velasco A, Vargas-García CA, Rojas-Morales FA, Castillo-Castelblanco S, Arguello-Fuentes H. Aplicaciones y retos del sensado remoto hiperespectral en la geología colombiana. Rev Fac Ing. 2015;24(40):17–29. DOI: https://doi.org/10.19053/01211129.3845

Roman-Gonzales A. Análisis de imágenes hiperespectrales. Rev Ing Desarro. 2013;9(35):14–7.

Shaw GA, Burke HK. Spectral Imaging for Remote Sensing. Lincoln Lab J. 2003;14(1):3–28.

Cerra D, Muller R, Reinartz P. Noise Reduction in Hyperspectral Images Through Spectral Unmixing. IEEE Geosci Remote Sens Lett [Internet]. 2014 Jan;11(1):109–13. Available from: https://ieeexplore.ieee.org/document/6488723/ DOI: https://doi.org/10.1109/LGRS.2013.2247562

Liu J, Wu Z, Xiao L, Sun J, Yan H. Generalized Tensor Regression for Hyperspectral Image Classification. IEEE Trans Geosci Remote Sens [Internet]. 2020 Feb;58(2):1244–58. Available from: https://ieeexplore.ieee.org/document/8877994/ DOI: https://doi.org/10.1109/TGRS.2019.2944989

Paoletti ME, Hautt J., Plaza J, Plaza A. Estudio Comparativo de Tecnicas de Clasificación de Imágenes Hiperespectrales. Rev Iberoam Automática e Informática Ind. 2019;(16):129–37. DOI: https://doi.org/10.4995/riai.2019.11078

Diezma B, Lleó L, Herrero A, Lunadei L, Roger JM, Ruiz-Altisent M. La imagen hiperespectral como herramienta de evaluación de la calidad de hortaliza de hoja mínimamente procesada. In: VI Congreso Ibérico en Agroingeniería. 2011. p. 1–9.

Li J, Li Y, Wang C, Ye X, Heidrich W. BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images. IEEE Trans Comput Imaging [Internet]. 2023;9:94–105. Available from: https://ieeexplore.ieee.org/document/10037221/ DOI: https://doi.org/10.1109/TCI.2023.3241549

Fan Y, Ni D, Ma H. HyperDB: a hyperspectral land class database designed for an image processing system. Tsinghua Sci Technol [Internet]. 2017 Feb;22(01):112–8. Available from: http://ieeexplore.ieee.org/document/7830901/ DOI: https://doi.org/10.1109/TST.2017.7830901

Banerjee A, Burlina P, Diehl C. A support vector method for anomaly detection in hyperspectral imagery. IEEE Trans Geosci Remote Sens [Internet]. 2006 Aug;44(8):2282–91. Available from: http://ieeexplore.ieee.org/document/1661816/ DOI: https://doi.org/10.1109/TGRS.2006.873019

Bannari A, Pacheco A, Staenz K, McNairn H, Omari K. Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data. Remote Sens Environ [Internet]. 2006 Oct;104(4):447–59. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0034425706002148 DOI: https://doi.org/10.1016/j.rse.2006.05.018

Lawrence RL, Wood SD, Sheley RL. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest). Remote Sens Environ [Internet]. 2006 Feb;100(3):356–62. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0034425705003792 DOI: https://doi.org/10.1016/j.rse.2005.10.014

Soto Bohorquez JC, Ruiz Reyes JM, Ipanaque Alama W, Chinguel Alama C. New Hyperspectral Index for Determining the State of Fermentation in the Non-Destructive Analysis for Organic Cocoa Violet. IEEE Lat Am Trans [Internet]. 2018 Sep;16(9):2435–40. Available from: https://ieeexplore.ieee.org/document/8789565/ DOI: https://doi.org/10.1109/TLA.2018.8789565

Kokaly RF, Hoefen TM, Graham GE, Kelley KD, Johnson MR, Hubbard BE, et al. Mineral information at micron to kilometer scales: Laboratory, field, and remote sensing imaging spectrometer data from the orange hill porphyry copper deposit, Alaska, USA. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) [Internet]. IEEE; 2016. p. 5418–21. Available from: http://ieeexplore.ieee.org/document/7730411/ DOI: https://doi.org/10.1109/IGARSS.2016.7730411

Fickus M, Lewis ME, Mixon DG, Peterson J. Compressive Hyperspectral Imaging for Stellar Spectroscopy. IEEE Signal Process Lett [Internet]. 2015 Nov;22(11):1829–33. Available from: http://ieeexplore.ieee.org/document/7115943/ DOI: https://doi.org/10.1109/LSP.2015.2433837

Della Porta CJ, Chang C-I. Progressive Compressively Sensed Band Processing for Hyperspectral Classification. IEEE Trans Geosci Remote Sens [Internet]. 2021 Mar;59(3):2378–90. Available from: https://ieeexplore.ieee.org/document/9123599/ DOI: https://doi.org/10.1109/TGRS.2020.3000873

Fen Chen, Ting Feng Tang, Ke Wang. Low-Rank Decomposition Model for Adaptive Identification of Similar Neighboring Pixels in Hyperspectral Images. IEEE Geosci Remote Sens Lett [Internet]. 2016 Feb;13(2):172–6. Available from: http://ieeexplore.ieee.org/document/7360895/ DOI: https://doi.org/10.1109/LGRS.2015.2504426

Wu Z, Shi L, Li J, Wang Q, Sun L, Wei Z, et al. GPU Parallel Implementation of Spatially Adaptive Hyperspectral Image Classification. IEEE J Sel Top Appl Earth Obs Remote Sens [Internet]. 2018 Apr;11(4):1131–43. Available from: https://ieeexplore.ieee.org/document/8066284/ DOI: https://doi.org/10.1109/JSTARS.2017.2755639

Krug LA, Platt T, Sathyendranath S, Barbosa AB. Ocean surface partitioning strategies using ocean colour remote Sensing: A review. Prog Oceanogr. 2017 Jun;155:41–53. DOI: https://doi.org/10.1016/j.pocean.2017.05.013

Rani M, Masroor M, Kumar P. Remote sensing of Ocean and Coastal Environment – Overview. In: Remote Sensing of Ocean and Coastal Environments. Elsevier; 2021. p. 1–15. DOI: https://doi.org/10.1016/B978-0-12-819604-5.00001-9

Wetherley EB, Roberts DA, Tague CL, Jones C, Quattrochi DA, McFadden JP. Remote sensing and energy balance modeling of urban climate variability across a semi-arid megacity. Urban Clim. 2021 Jan;35:100757. DOI: https://doi.org/10.1016/j.uclim.2020.100757

Ganci G, Cappello A, Bilotta G, Del Negro C. How the variety of satellite remote sensing data over volcanoes can assist hazard monitoring efforts: The 2011 eruption of Nabro volcano. Remote Sens Environ. 2020 Jan;236:111426. DOI: https://doi.org/10.1016/j.rse.2019.111426

Fu X, Yao L, Xu W, Wang Y, Sun S. Exploring the multitemporal surface urban heat island effect and its driving relation in the Beijing-Tianjin-Hebei urban agglomeration. Appl Geogr. 2022 Jul;144:102714. DOI: https://doi.org/10.1016/j.apgeog.2022.102714

Chanchí Golondrino GE, Ospina Alarcón MA, Saba M. Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics. Atmosphere (Basel) [Internet]. 2023 Jul 14;14(7):1148. Available from: https://www.mdpi.com/2073-4433/14/7/1148 DOI: https://doi.org/10.3390/atmos14071148

Saha D, Manickavasagan A. Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Curr Res Food Sci. 2021;4:28–44. DOI: https://doi.org/10.1016/j.crfs.2021.01.002

Spilsbury MJ, Euceda A. Transformada Rápida de Fourier. Rev la Esc Física. 2016;4(2):45–52. DOI: https://doi.org/10.5377/ref.v4i2.8276

Bonafonte A. Señales y Sistemas I. Signals. Catalunya: Universidad politécnica de Catalunya; 2009. 1–81 p.

Received 2024-01-12
Accepted 2024-10-08
Published 2024-10-08