A Near-Real-Time Flood Detection Method Based on Deep Learning and SAR Images

Abstract

Owning to the nature of flood events, near-real-time flood detection and mapping is essential for disaster prevention, relief, and mitigation. In recent years, the rapid advancement of deep learning has brought endless possibilities to the field of flood detection. However, deep learning relies heavily on training samples and the availability of high-quality flood datasets is rather limited. The present study collected 16 flood events in the Yangtze River Basin and divided them into three categories for different purpose: training, testing, and application. An efficient methodology of dataset-generation for training, testing, and application was proposed. Eight flood events were used to generate strong label datasets with 5296 tiles as flood training samples along with two testing datasets. The performances of several classic convolutional neural network models were evaluated with those obtained datasets, and the results suggested that the efficiencies and accuracies of convolutional neural network models were obviously higher than that of the threshold method. The effects of VH polarization, VV polarization, and the involvement of auxiliary DEM on flood detection were investigated, which indicated that VH polarization was more conducive to flood detection, while the involvement of DEM has a limited effect on flood detection in the Yangtze River Basin. Convolutional neural network trained by strong datasets were used in near-real-time flood detection and mapping for the remaining eight flood events, and weak label datasets were generated to expand the flood training samples to evaluate the possible effects on deep learning models in terms of flood detection and mapping. The experiments obtained conclusions consistent with those previously made on experiments with strong datasets.

A Two-Stage SAR Image Generation Algorithm Based on GAN with Reinforced Constraint Filtering and Compensation Techniques

“…Finally, we return the common part of S AC and S D to G, according to which G updates its parameters. The three steps above can be summarized as: (1) Calculating the distance between the predictions and the input labels, then retaining the images whose distance ranks in top-k. (2) Sorting and retaining images whose scores rank in top-k. (3) Finding their common part and returning to the generator. The three steps are illustrated in Figure 2.…”

“…Synthetic aperture radar (SAR) enjoys a good reputation in the domain of remote sensing due to its imaging capability which is independent of flight altitude and weather condition. It is widely used in environmental surveillance [1], military reconnaissance [2], automatic target recognition (ATR) [3], crop monitoring [4] and other civil use [5].…”

Flood Detection with SAR: A Review of Techniques and Datasets

“…Nevertheless, the literature produced several studies using DL methodologies for flood mapping using SAR data. The performance of global thresholding-based approaches and those of some popular DL architectures are compared in [101], obtaining convincing results using a dataset composed by 32 Sentinel-1 SAR images corresponding to 16 flood events that occurred in the Yangtze River Basin (China). The authors claimed that the better performance was achieved by the UNet architecture, which is probably the most adopted solution in the literature, eventually with some modifications against the basic structure introduced in [102] and reported in Figure 3.…”

Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

“…Remote sensing data significantly transformed fluvial geomorphology in the last decade [14], with most applications involving multi-spectral passive aerial or satellite imagery [90]. Active SAR satellite data such as Sentinel-1 imagery have been demonstrated to provide a valuable asset to map inundated areas [49,91,92], with high accuracy of the water mapping when compared to other sources.…”