Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Abstract

Abstract This study develops an effective and robust method to mine bitemporal and dual‐polarization synthetic aperture radar (SAR) imagery information for coastal inundation mapping, based on deep convolutional neural networks. The specially tailored deep convolutional neural network‐based SAR coastal flooding mapping network (SARCFMNet) leverages two modifications to improve the accuracy and robustness: the physics‐aware input information design and the regularization. The proposed SARCFMNet is applied to the mapping and impact analysis of the coastal inundation caused by 2017 Hurricane Harvey near Houston, Texas, USA. Six pairs of Sentinel‐1 SAR images are analyzed along with corresponding ground truth data from Copernicus Emergency Management Service Rapid Mapping products and land‐cover types from Google Earth and OpenStreetMap. Flooded areas of 4,000 km 2 are extracted and analyzed. In an analyzed scene, 76% of the flooded area was agriculture area like pasture and cultivated crops field. A flooding map series shows the inundation shrinking rate is about 1% of the analyzed scene per day after the passage of Harvey. However, there was a delayed inundation in Glen Flora, Texas, after the heavy raining period. The average mapping accuracy and F1 score, that is, the harmonic mean of recall and precision, are 0.98 and 0.88, respectively. The impact of wind and cost‐sensitive loss functions on the development of SARCFMNet is also discussed. This study demonstrates the proposed method can accurately map hurricane‐induced inundation. The method can also be readily extended to other multitemporal SAR imagery classification applications.