Water–Land Segmentation via Structure-Aware CNN–Transformer Network on Large-Scale SAR Data

Abstract

Water–land segmentation has important applications in many aspects, such as environmental protection and flood prevention. Synthetic aperture radar (SAR) is one of the important tools for watershed monitoring. However, neither the traditional SAR water–land segmentation methods nor convolutional neural network (CNN)-based algorithms have satisfactory accuracy for the boundaries of water, especially for fine rivers/lakes. Transformer can effectively extract global information and has achieved amazing success in image interpretation. In this article, a structure-aware CNN–Transformer network with U-shaped structure is designed to solve the water segmentation problem, named Trans-SANet. Also, an edge refinement postprocessing module is added to Trans-SANet to refine the water edges, named Trans-SANet+. First, a cascaded attentional upsampler (CAUP) is designed in the decoder to combine spatial and temporal attentional serialization to fully capture feature information and avoid feature omission. Second, a structure-aware loss (SA-Loss) function is designed to give sufficient attention to the overall boundary structure while avoiding the loss of small-scale features to reduce the segmentation error of the boundary, thus improving the segmentation accuracy of the fine water region. Finally, combining fully connected conditional random field (CRF) with edge extraction, a postprocessing module is proposed to further optimize the results of water–land segmentation. Experiments on three SAR datasets show that Trans-SANet+ outperforms several most advanced depth segmentation networks in terms of accuracy and speed.