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Year 2022, Volume: 7 Issue: 1, 81 - 90, 15.02.2022
https://doi.org/10.26833/ijeg.882589

Abstract

References

  • Aslan M F, Durdu A & Sabanci K (2019). Fusion of CT and MR Liver Images by SURF-Based Registration. International Journal of Intelligent Systems and Applications in Engineering, 7(4), SE-Research Article). doi:10.18201/ijisae.2019457233
  • Foumelis M, Blasco J M D, Desnos Y, Engdahl M, Fernandez D, Veci L, … Wong C (2018). Esa Snap - Stamps Integrated Processing for Sentinel-1 Persistent Scatterer Interferometry. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 1364–1367. doi:10.1109/IGARSS.2018.8519545
  • Ghorbanzadeh O, Didehban K, Rasouli H, Kamran K V, Feizizadeh, B., & Blaschke, T. (2020). An application of sentinel-1, sentinel-2, and GNSS data for landslide susceptibility mapping. ISPRS International Journal of Geo-Information, 9(10), 561.
  • Hemdan E E-D (2021). An efficient and robust watermarking approach based on single value decompression, multi-level DWT, and wavelet fusion with scrambled medical images. Multimedia Tools and Applications, 80(2), 1749–1777. doi:10.1007/s11042-020-09769-7
  • James A P & Dasarathy B V (2014). Medical image fusion: A survey of the state of the art. Information Fusion, 19, 4–19. doi:https://doi.org/10.1016/j.inffus.2013.12.002
  • Khaleghi B, Khamis A, Karray F O & Razavi S N (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44. doi:https://doi.org/10.1016/j.inffus.2011.08.001
  • Kulkarni S C & Rege P P (2020). Pixel level fusion techniques for SAR and optical images: A review. Information Fusion, 59, 13–29. doi:https://doi.org/10.1016/j.inffus.2020.01.003
  • Pajares G & Manuel de la Cruz J (2004). A wavelet-based image fusion tutorial. Pattern Recognition, 37(9), 1855–1872. doi:https://doi.org/10.1016/j.patcog.2004.03.010
  • Rajah P, Odindi J & Mutanga O (2018). Feature level image fusion of optical imagery and Synthetic Aperture Radar (SAR) for invasive alien plant species detection and mapping. Remote Sensing Applications: Society and Environment, 10, 198–208. doi:https://doi.org/10.1016/j.rsase.2018.04.007
  • Shah E, Jayaprasad P & James M E (2019). Image fusion of SAR and optical images for identifying antarctic ice features. Journal of the Indian Society of Remote Sensing, 47(12), 2113–2127.
  • Veloso A, Mermoz S, Bouvet A, Le Toan T, Planells M, Dejoux J-F & Ceschia E (2017). Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199, 415–426. doi:https://doi.org/10.1016/j.rse.2017.07.015
  • Yommy A S, Liu R & Wu A S (2015). SAR Image Despeckling Using Refined Lee Filter. In 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2, 260–265. doi:10.1109/IHMSC.2015.236

Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets

Year 2022, Volume: 7 Issue: 1, 81 - 90, 15.02.2022
https://doi.org/10.26833/ijeg.882589

Abstract

In this study, the fusion of optical and synthetic aperture radar images with wavelet transform was investigated. Images are obtained from Sentinel-1 and sentinel-2 satellites. Images were decomposed by wavelet transform. The four main coefficients were obtained for different wavelet packages and up to ten decomposition levels. The coefficients were combined taking the maximum, minimum or mean. 1710 Fused images were obtained for all possible combinations in terms of different wavelet packets, decomposition levels and fusion rules. Fused images were evaluated according to the structural similarity index (SSI). It was seen that the missing regions in the optical images were improved in the fused images with the appropriate wavelet packets and highest SSI.

References

  • Aslan M F, Durdu A & Sabanci K (2019). Fusion of CT and MR Liver Images by SURF-Based Registration. International Journal of Intelligent Systems and Applications in Engineering, 7(4), SE-Research Article). doi:10.18201/ijisae.2019457233
  • Foumelis M, Blasco J M D, Desnos Y, Engdahl M, Fernandez D, Veci L, … Wong C (2018). Esa Snap - Stamps Integrated Processing for Sentinel-1 Persistent Scatterer Interferometry. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 1364–1367. doi:10.1109/IGARSS.2018.8519545
  • Ghorbanzadeh O, Didehban K, Rasouli H, Kamran K V, Feizizadeh, B., & Blaschke, T. (2020). An application of sentinel-1, sentinel-2, and GNSS data for landslide susceptibility mapping. ISPRS International Journal of Geo-Information, 9(10), 561.
  • Hemdan E E-D (2021). An efficient and robust watermarking approach based on single value decompression, multi-level DWT, and wavelet fusion with scrambled medical images. Multimedia Tools and Applications, 80(2), 1749–1777. doi:10.1007/s11042-020-09769-7
  • James A P & Dasarathy B V (2014). Medical image fusion: A survey of the state of the art. Information Fusion, 19, 4–19. doi:https://doi.org/10.1016/j.inffus.2013.12.002
  • Khaleghi B, Khamis A, Karray F O & Razavi S N (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44. doi:https://doi.org/10.1016/j.inffus.2011.08.001
  • Kulkarni S C & Rege P P (2020). Pixel level fusion techniques for SAR and optical images: A review. Information Fusion, 59, 13–29. doi:https://doi.org/10.1016/j.inffus.2020.01.003
  • Pajares G & Manuel de la Cruz J (2004). A wavelet-based image fusion tutorial. Pattern Recognition, 37(9), 1855–1872. doi:https://doi.org/10.1016/j.patcog.2004.03.010
  • Rajah P, Odindi J & Mutanga O (2018). Feature level image fusion of optical imagery and Synthetic Aperture Radar (SAR) for invasive alien plant species detection and mapping. Remote Sensing Applications: Society and Environment, 10, 198–208. doi:https://doi.org/10.1016/j.rsase.2018.04.007
  • Shah E, Jayaprasad P & James M E (2019). Image fusion of SAR and optical images for identifying antarctic ice features. Journal of the Indian Society of Remote Sensing, 47(12), 2113–2127.
  • Veloso A, Mermoz S, Bouvet A, Le Toan T, Planells M, Dejoux J-F & Ceschia E (2017). Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199, 415–426. doi:https://doi.org/10.1016/j.rse.2017.07.015
  • Yommy A S, Liu R & Wu A S (2015). SAR Image Despeckling Using Refined Lee Filter. In 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2, 260–265. doi:10.1109/IHMSC.2015.236
There are 12 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Huseyin Duysak 0000-0002-2748-0660

Enes Yiğit 0000-0002-0960-5335

Publication Date February 15, 2022
Published in Issue Year 2022 Volume: 7 Issue: 1

Cite

APA Duysak, H., & Yiğit, E. (2022). Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets. International Journal of Engineering and Geosciences, 7(1), 81-90. https://doi.org/10.26833/ijeg.882589
AMA Duysak H, Yiğit E. Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets. IJEG. February 2022;7(1):81-90. doi:10.26833/ijeg.882589
Chicago Duysak, Huseyin, and Enes Yiğit. “Investigation of the Performance of Different Wavelet-Based Fusions of SAR and Optical Images Using Sentinel-1 and Sentinel-2 Datasets”. International Journal of Engineering and Geosciences 7, no. 1 (February 2022): 81-90. https://doi.org/10.26833/ijeg.882589.
EndNote Duysak H, Yiğit E (February 1, 2022) Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets. International Journal of Engineering and Geosciences 7 1 81–90.
IEEE H. Duysak and E. Yiğit, “Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets”, IJEG, vol. 7, no. 1, pp. 81–90, 2022, doi: 10.26833/ijeg.882589.
ISNAD Duysak, Huseyin - Yiğit, Enes. “Investigation of the Performance of Different Wavelet-Based Fusions of SAR and Optical Images Using Sentinel-1 and Sentinel-2 Datasets”. International Journal of Engineering and Geosciences 7/1 (February 2022), 81-90. https://doi.org/10.26833/ijeg.882589.
JAMA Duysak H, Yiğit E. Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets. IJEG. 2022;7:81–90.
MLA Duysak, Huseyin and Enes Yiğit. “Investigation of the Performance of Different Wavelet-Based Fusions of SAR and Optical Images Using Sentinel-1 and Sentinel-2 Datasets”. International Journal of Engineering and Geosciences, vol. 7, no. 1, 2022, pp. 81-90, doi:10.26833/ijeg.882589.
Vancouver Duysak H, Yiğit E. Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets. IJEG. 2022;7(1):81-90.

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