192
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Multi-stage guided-filter for SAR and optical satellites images fusion using Curvelet and Gram Schmidt transforms for maritime surveillance

ORCID Icon, , ORCID Icon & ORCID Icon
Pages 38-57 | Received 17 Aug 2021, Accepted 03 Nov 2021, Published online: 15 Nov 2021

References

  • Bioresita, F., et al., 2019. Fusion of sentinel-1 and sentinel-2 image time series for permanent and temporary surface water mapping. International Journal of Remote Sensing, 40 (23), 9026–9049.
  • Chang, N.-B. and Bai, K., 2018. Multisensor data fusion and machine learning for environmental remote sensing. Boca Raton: CRC Press.
  • Choi, J., Park, H., and Seo, D., 2019. Pansharpening using guided filtering to improve the spatial clarity of VHR satellite imagery. Remote Sensing, 11 (6), 633. doi:10.3390/rs11060633.
  • Chu, T., et al., 2020. Novel fusion method for SAR and optical images based on non-subsampled shearlet transform. International Journal of Remote Sensing, 41 (12), 4590–4604. doi:10.1080/01431161.2020.1723175.
  • Crété-Roffet, F., et al., 2007. The blur effect: perception and estimation with a new no-reference perceptual blur metric. In: Human vision and electronic imaging, San Jose, CA, United States: Society of Photo-Optical Instrumentation Engineers (SPIE), 12.
  • Eltanany, A.S., Amein, A.S., and Elwan, M.S., 2021. A modified corner detector for SAR images registration. International Journal of Engineering Research in Africa, 53, 123–156. doi:10.4028/scientific.net/JERA.53.123
  • Filipponi, F., 2019. Sentinel-1 GRD preprocessing workflow. Proceedings, 18 (1), 11. doi:10.3390/ECRS-3-06201.
  • Hadzagic, M., Isabelle, M., and Kashyap, N., Hard and soft data fusion for maritime traffic monitoring using the integrated Ornstein-Uhlenbeck process. ed. IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Victoria, BC, Canada, 2020, 98–105.
  • He, K., Sun, J., and Tang, X., 2013. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (6), 1397–1409. doi:10.1109/TPAMI.2012.213.
  • Iervolino, P., et al., 2019. A novel multispectral, panchromatic and SAR data fusion for land classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (10), 3966–3979. doi:10.1109/JSTARS.2019.2945188.
  • Kanjir, U., Greidanus, H., and Oštir, K., 2018. Vessel detection and classification from spaceborne optical images: a literature survey. Remote Sensing of Environment, 207, 1–26. doi:10.1016/j.rse.2017.12.033
  • Kulkarni, S.C., and Rege, P.P., Fusion of RISAT-1 SAR image and resourcesat-2 multispectral images using wavelet transform. ed. 6th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2019, 45–52.
  • Kulkarni, S.C. and Rege, P.P., 2020. Pixel level fusion techniques for SAR and optical images: a review. Information Fusion, 59, 13–29. doi:10.1016/j.inffus.2020.01.003
  • Ma, X., et al., 2019. Remote sensing image fusion based on sparse representation and guided filtering. Electronics, 8 (3), 303–320. doi:10.3390/electronics8030303.
  • Mahyouba, S., et al., 2019. Fusing of optical and synthetic aperture radar (SAR) remote sensing data: a systematic literature review (SLR). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42 (4/W12, 127–138).
  • Mangalraj, P., et al., 2020. A review of multi-resolution analysis (MRA) and multi-geometric analysis (MGA) tools used in the fusion of remote sensing images. Circuits, Systems, and Signal Processing, 39 (6), 3145–3172. doi:10.1007/s00034-019-01316-6.
  • Marchetti, P.G., Soille, P., and Bruzzone, L. 2016. A special issue on big data from space for geoscience and remote sensing IEEE Geoscience and Remote Sensing Magazine, 4( 3),7–9.
  • Meng, X., et al. 2016. Pansharpening with a guided filter based on three-layer decomposition. Sensors, 16 (7), 1068. doi:10.3390/s16071068.
  • Pohl, C., and Van Genderen, J., 2016. Remote sensing image fusion: a practical guide. London, New York: CRC Press.
  • Rodger, M. and Guida, R., 2020. Classification-Aided SAR and AIS data fusion for space-based maritime surveillance. Remote Sensing, 13 (1), 104. doi:10.3390/rs13010104.
  • Rodger, M. and Guida, R., 2021. Classification-Aided SAR and AIS data fusion for space-based maritime surveillance. Remote Sensing, 13 (1), 104.
  • Yang, Y., et al., 2016. Remote sensing image fusion based on adaptive IHS and multiscale guided filter. IEEE Access, 4, 4573–4582. doi:10.1109/ACCESS.2016.2599403.
  • Zhang, R., et al., 2020. A novel feature-level fusion framework using optical and SAR remote sensing images for land use/land cover (LULC) classification in cloudy mountainous area. Applied Sciences, 10 (8), 2928. doi:10.3390/app10082928.
  • Zheng, L., et al., SAR and optical image fusion for coastal surveillance. ed. IEEE International Geoscience and Remote Sensing Symposium IGARSS, Yokohama, Japan, 2019, 2802–2805.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.