Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion

Abstract

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

Testing Urban Flood Mapping Approaches from Satellite and In-Situ Data Collected during 2017 and 2019 Events in Eastern Canada

“…Previous studies report good agreement between CCD flood extents and near coincident high-resolution optical images, crowdsourced reference points and inundation models [18]. Combined coherence and intensity achieved between 0.68 and 0.72 kappa in Li et al [48], which is greater than the kappa of 0.58 we achieved using coherence only ( Table 5). A second study using coherence and intensity achieved overall accuracies of 70% against hydraulic model simulations using intensity only, increasing to 79% when incorporating coherence [49].…”

“…For urban CCD, as RCM data become more readily available, higher spatial and temporal resolutions will be evaluated over urban flood events. Promising results have been achieved integrating coherence and intensity data together [48,49]. We believe that a synergistic relationship between intensity and coherence could also be exploited with the aforementioned benefits associated with RCM data.…”

Synthetic Aperture Radar Flood Detection under Multiple Modes and Multiple Orbit Conditions: A Case Study in Japan on Typhoon Hagibis, 2019

“…The dominant scatterers of the architectures were sometimes on the roof and could not be used to determine whether the area was flooded. Therefore, a more precise analysis and discussion would consider the building heights [26]. Those reasons that cause the detection ratio to become worse are common with the ideal Stripmap-based analysis.…”

“…Next, we calculated the coherence value with a 5 × 5 moving window and extracted the flooded area with a threshold of dγ = 0.3. These values were experimentally derived from preceding research [26,40,41]. Finally, we applied an iterative median filter with a 5 × 5 window for segmentation and acquired an inundation map.…”

“…Various studies have been performed to increase the accuracy of flood detection [16][17][18][19]. Multiple sensors combine to provide external data [20][21][22][23], the stacking of pre-flood observations [24,25], and applying machine learning, which are the typical methods used for increasing flood detection accuracies [26][27][28]. Furthermore, advanced segmentation schemes have been proposed [29,30], as well as the use of polarimetry [31].…”