High-Performance Segmentation for Flood Mapping of HISEA-1 SAR Remote Sensing Images

Abstract

Floods are the among the most frequent and common natural disasters, causing numerous casualties and extensive property losses worldwide every year. Since flooding areas are often accompanied by cloudy and rainy weather, synthetic aperture radar (SAR) is one of the most powerful sensors for flood monitoring with capabilities of day-and-night and all-weather imaging. However, SAR images are prone to high speckle noise, shadows, and distortions, which affect the accuracy of water body segmentation. To address this issue, we propose a novel Modified DeepLabv3+ model based on the powerful extraction ability of convolutional neural networks for flood mapping from HISEA-1 SAR remote sensing images. Specifically, a lightweight encoder MobileNetv2 is used to improve floodwater detection efficiency, small jagged arrangement atrous convolutions are employed to capture features at small scales and improve pixel utilization, and more upsampling layers are utilized to refine the segmented boundaries of water bodies. The Modified DeepLabv3+ model is then used to analyze two severe flooding events in China and the United States. Results show that Modified DeepLabv3+ outperforms competing semantic segmentation models (SegNet, U-Net, and DeepLabv3+) with respect to the accuracy and efficiency of floodwater extraction. The modified model training resulted in average accuracy, F1, and mIoU scores of 95.74%, 89.31%, and 87.79%, respectively. Further analysis also revealed that Modified DeepLabv3+ is able to accurately distinguish water feature shape and boundary, despite complicated background conditions, while also retaining the highest efficiency by covering 1140 km2 in 5 min. These results demonstrate that this model is a valuable tool for flood monitoring and emergency management.