Flooded Area Detection Method Based on Fusion of Optical and SAR Remote Sensing Images

WANG Zhihao; LI Gang; JIANG Xiao

doi:10.12000/JR19095

Volume 9 Issue 3

Jun. 2020

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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

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 CSTR: 32380.14.JR19095

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Funds: The National Natural Science Foundation of China (61790551 and 61925106), Civil Space Advance Research Program of China (D010305)

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Corresponding author: LI Gang, gangli@tsinghua.edu.cn
Received Date: 2019-11-05
Rev Recd Date: 2020-02-18

Available Online: 2020-03-07

Publish Date: 2020-06-01

Abstract

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.
- Remote sensing images fusion,
- Optical,
- Synthetic Aperture Radar (SAR),
- Hierarchical Fuzzy C-Means (H-FCM),
- Hierarchical clustering,
- Region-growing,
- Spatial constraint

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References(34)

References

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Flooded Area Detection Method Based on Fusion of Optical and SAR Remote Sensing Images

DOI: 10.12000/JR19095 CSTR: 32380.14.JR19095