AFUNet With Active Contour Loss for Water Body Detection in SAR Imagery

With advancements in remote sensing technology, synthetic aperture radar (SAR) imagery has become one of the main methods to detect surface water bodies. The detection of water bodies in SAR imagery remains a challenging task due to the presence of complex interference. To achieve accurate water body detection, we proposed an attention fusion U-net inspired by the effectiveness of U-net in segmenting small targets with weak edges. First, the spatial attention module and channel attention module are added to the skip connections between encoder and decoder parts to extract useful low- and high-level features, thereby compensating for the loss of semantic information of downsampling. Second, the multiscale convolutional pooling block is introduced into the encoder part to better utilize the contextual information, capturing water and land features at different scales. Third, considering the feature distortion resulting from upsampling, an attentional upsampler (AU) is designed to facilitate lossless feature fusion. Furthermore, an active contour loss is designed as additional regularization to learn more boundary information, improving the model's segmentation performance. The water body detection experiments on the ALOS phased array L-band SAR and Sen1-SAR datasets demonstrate that the presented AFUNet outperforms the related start-of-the-art methods in detection accuracy in terms of five evaluation metrics.