Fourier Analysis Approach to Identify Water Bodies Through Hyperspectral Imagery
Abstract
Unlike conventional images, which have three channels of information, hyperspectral images are composed of many spectral channels that provide detailed information about the materials present in them. Thus, considering their great potential to monitor changes in the environment and the importance of freshwater bodies for life and nature, it is relevant to propose and evaluate the effectiveness of different computational methods focused on detecting bodies of water in hyperspectral images; therefore, this research proposes and evaluates a computational method based on Fourier phase similarity. To do so, four methodological phases were defined, namely: exploration and selection of open-source technologies for hyperspectral image analysis, determination of the characteristic pixel of water bodies, calculation of Fourier phase similarity between the representative pixel of water bodies and the 200 sample pixels chosen from water bodies and other materials, and verification of the method on a test hyperspectral image. Spectral, NumPy, and Pandas libraries of Python were used to implement the proposed method, which resulted, for the first 170 bands, on an average phase similarity of 99.46% with respect to water body pixels and a minimum phase similarity with water body pixels of 93.01%. The results show that the proposed method is effective to detect water body pixels and can be used or extrapolated as an alternative to detection methods based on correlation metrics and machine learning.
Keywords
Computer vision, Fourier analysis, hyperspectral imaging, water bodies detection, remote sensing, machine learning
References
- 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