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Implementing Geomatics Techniques for the Increase of Resolution of Satellite Images

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Języki publikacji
EN
Abstrakty
EN
Image enhancement is the process of improving the quality of a digital image. Improved image quality is one of the goals of the ongoing effort to improve the information content and interpretability of satellite images. Two sets of remote sensing data were collected for this region: one from the SPOT-4 (2018) satellite and the other from the Enhanced Thematic Mapper Plus (ETM+) Landsat 7 (2020) satellite. This study used two images taken at various spatial resolutions of the same location. Two images are shown here: one with a 30 m spatial resolution and the other with a 5 m resolution with multispectral processing. The results indicate that integrating spatial and spectral resolution using geomatics techniques significantly benefits various applications. Before merging the images, it was root mean square error (RMSE) 11.55 Easting, 5.77 Northing and became 1.52 Easting, 1.45 Northing after merging the images. After implementing the approach, the resulting fusing image exhibits enhanced spatial resolution, and the resulting multispectral image has excellent spatial as well as spectral resolution. Finally, the improved combined image with great spatial and spectral resolution is prepared for analysis and classification.
Twórcy
  • Technical Institute of Babylon, Al-Furat Al-Awsat Technical University, Kufa, Iraq
  • Surveying Department, Technical College-Baghdad, Middle Technical University, Baghdad, Iraq
  • Civil Engineering, University of Technology, Baghdad, Iraq
Bibliografia
  • 1. Ahmed, S., Salih, D. 2022. IHS image fusion based on Gray Wolf Optimizer (GWO). Anbar Journal for Engineering Sciences, 13(1), 65–75. https://doi.org/10.37649/aengs.2022.175882
  • 2. Al-Jasim, A.A.N., Naji, T.A., Shaban, A.H. 2022. The effect of using the different satellite spatial resolution on the fusion technique. Iraqi Journal of Science, 63(9), 4131–4141. https://doi.org/10.24996/ijs.2022.63.9.40
  • 3. Al-Rubiey, I.J.M. 2017. Increase the intelligibility of multispectral image using pan-sharpening techniques for many remotely sensed images. Ibn AL-Haitham Journal For Pure and Applied Science, 28(3).
  • 4. Al-Saedi, A.S.J., Kadhum, Z.M., Jasim, B.S. 2023. Land use and land cover analysis using geomatics techniques in Amara City. Ecol. Eng, 9, 161–169.
  • 5. Rahmawati, A.Y. 2020. Title No Title No Title. 32(July), 1–23.
  • 6. Aqeel, A.F. 2012. Locating drainage pattern for qaraqosh valley by merging ETM + with SPOT satellite image. 53(December), 1175–1180.
  • 7. Jasim, B.S., Al-Saedi, A.S.J., Kadhum, Z.M. 2024a. Using remote sensing application for verification of thematic maps produced based on high-resolution satellite images. AIP Conference Proceedings, 3092(1).
  • 8. Jasim, B.S., Jasim, O.Z., AL-Hameedawi, A.N. 2024b. A review for vegetation vulnerability using artificial intelligent (AI) techniques. AIP Conference Proceedings, 3092(1).
  • 9. Jensen, J.R. 2005. Digital image processing: a remote sensing perspective. Upper Saddle River, NJ: SPrentice Hall.
  • 10. Kadhum, Z.M., Jasim, B.S., Al-saedi, A.S.J. 2023. Improving the spectral and spatial resolution of satellite image using geomatics techniques improving the spectral and spatial resolution of satellite image using geomatics techniques, 040011.
  • 11. Kaittan, M.Q. 2018. Improve the spatial resolution of multispectral satellite image using different image sharpening techniques. Iraqi Journal of Science, 59(1A), 227–232. https://doi.org/10.24996/IJS.2018.59.1A.24
  • 12. Karwowska, K., Wierzbicki, D. 2022a. Improving spatial resolution of satellite imagery using generative adversarial networks and window functions. Remote Sensing, 14(24). https://doi.org/10.3390/rs14246285
  • 13. Karwowska, K., Wierzbicki, D. 2022b. Using superresolution algorithms for small satellite imagery: A systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 3292–3312. https://doi.org/10.1109/JSTARS.2022.3167646
  • 14. Melissa, S., Sheida, R., Daria, M. 2008. Evaluation of pan-sharpening methods. Journal of Mathematics Departmen in UCLA, 15(2), 250–256.
  • 15. Merchant, J.W., Narumalani, S. 2009. Integrating remote sensing and geographic information systems. In The SAGE handbook of remote sensing, 257–268. SAGE Publications Ltd: London, UK.
  • 16. Mora, L.F., Fernández, L.aR., Verdú, F.A., Kyun, I.S. 2012. Using a wavelet based method for high resolution satellite image fusion. Methods, 53(4), 999–1005.
  • 17. Pollpeter, K., Barrett, E. 2021. NATO ally contributions to the space domain. Montgomery: China Aerospace Studies Institute.
  • 18. Rocchini, D. 2007. Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sensing of Environment, 111(4), 423–434. https://doi.org/10.1016/j.rse.2007.03.018
  • 19. Vrabel, J. 1996. Multispectral imagery band sharpening study. Photogrammetric Engineering and Remote Sensing, 62(9), 1075–1084.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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