HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images

Abstract

Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively.

GEA-MSNet: A Novel Model for Segmenting Remote Sensing Images of Lakes Based on the Global Efficient Attention Module and Multi-Scale Feature Extraction

“…However, with the continuous advancement of attention mechanism, although the novel attention modules can enhance the segmentation accuracy in models, they also introduce a significant increase in parameter count. In their study, Song et al [50] explored the integration of various attention models into the U-Net network and conducted experiments. While achieving an improvement of 4~5% in segmentation accuracy, the augmented number of parameters due to attention models emerged as a new challenge, thereby directing their future efforts towards further enhancements.…”

“…The spatial attention mechanism CA [57] employed here differs from self-attention in terms of feature calculation through matrices. CA focuses more on capturing spatial information and channel relationships within features [50].…”

MSAFNet: Multiscale Successive Attention Fusion Network for Water Body Extraction of Remote Sensing Images

“…MSNANet proposed the MSNA and OASPP modules, which effectively merge multiscale water body features and refine the representation by leveraging contextual information to improve the models' water body extraction accuracy [57]. HA-Unet enhances the ability to capture shallow feature details and enhance global contextual information through the joint use of local attention and self-attention, achieving effective extraction of complex urban water bodies [58].…”

Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites

“…Floods are one of the common natural disasters, mainly caused by short periods of extremely heavy precipitation, which seriously threaten the safety of people's property and life. For example, the flood in South Asia in 2020 lasts for about six months [1], and the flood in Zhengzhou, China in 2021 caused damage to many buildings [2,3]. Thus, accurate temporal and spatial information based on GNSS technology should be provided in order to improve the efficiency of rescue during floods and to protect human life and property [4,5].…”