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| Citation: | WANG Zhihao, LI Gang, and JIANG Xiao. Flooded area detection method based on fusion of optical and SAR remote sensing images[J]. Journal of Radars, 2020, 9(3): 539–553. doi: 10.12000/JR19095
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Flooded Area Detection Method Based on Fusion of Optical and SAR Remote Sensing Images
DOI: 10.12000/JR19095
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Abstract
The flooded area detection method based on the fusion of optical and Synthetic Aperture Radar (SAR) images is applicable for all weather conditions and times. However, due to the large number of randomly distributed intensive speckle noise in SAR images, the conventional methods of detection often trigger high false alarm rates at flood-stricken zones. Inspired by the Fuzzy C-Means (FCM) clustering method, a hierarchical clustering algorithm (Hierarchical Fuzzy C-Means, H-FCM) is proposed in this paper. This method fuses the SAR image captured after the flood with the optical image captured before the flood. Based on the fused image, this method uses the proposed hierarchical clustering model to obtain the preliminary detection results of the flooded area. Additionally, the algorithm uses the proposed region-growing algorithm to obtain the river location before the flood and uses it as a spatial constraint for the preliminary detection results to further screen out suspected flooded areas and significantly improve detection performance. The experimental data used in this paper include the remote sensing images captured before and after the Gloucester floods in the United Kingdom in 1999, as well as the remote sensing images captured before and after the Nanchang floods in China in 2019. The effectiveness and validity of the H-FCM algorithm are also supported by comparison experiments. -
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References
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- Figure 1. The GF4 image of Typhoon Lekima (Resource Satellite Centre)
- Figure 2. The GF3 image of Typhoon Lekima (Resource Satellite Centre)
- Figure 3. Spectral characteristic curves of typical features[23]
- Figure 4. SAR image Ea after the flood[10]
- Figure 5. Near-infrared image Ei before the flood[10]
- Figure 6. Direction matrix Jd1(down)
- Figure 7. Direction matrix Jd2(up)
- Figure 8. Direction matrix Jd3(left)
- Figure 9. Direction matrix Jd4(right)
- Figure 10. Threshold graph Pt
- Figure 11. Definition of four-connected regions
- Figure 12. Initial growth point location
- Figure 13. Preliminary growth results
- Figure 14. Road recognition results
- Figure 15. River location Eb before the flood
- Figure 16. Fusion post-disaster SAR image Fu
- Figure 17. Clustering model Gm7
- Figure 18. Analysis of sparsity of clustering model
- Figure 19. Preliminary clustering results Gr7
- Figure 20. Spatial constraint curve of flooded area
- Figure 21. Final experimental result FF
- Figure 22. NDVI images after the flood[10]
- Figure 23. Manually labeled truth map
- Figure 24. 3-D map of the distribution of pixel values
- Figure 25. Edge profile of Ea
- Figure 26. Detection results of flooded area based on four algorithms
- Figure 27. Manually labeled flooded areas
- Figure 28. Sentinel 1, 2 data before and after flood
- Figure 29. Detection results of flooded area based on four algorithms
- Figure 30. Detection results of flooded area based on four algorithms after RL filtering