Multiscale Location Attention Network for Building and Water Segmentation of Remote Sensing Image

Traditional building and water segmentation methods are vulnerable to noise interference, and hence they could not avoid missed and false detections in the detection process. Excessive deep learning downsampling would lead to significant loss of feature map information, and image location information offset, and the overall effect of falling apart. To address these issues, a Multi-Scale Location Attention Network (MSLA) is proposed. Location-spatial information and channel information are particularly important for edge detail segmentation in building and water cover. The network includes a Location Channel Attention Unit (LCA) to focus on tributary details of rivers and segmentation of building edge eaves. Moreover, this paper builds a Dual-Branch Multi-Scale Aggregation Unit (DBMSA) to obtain deeper multi-scale semantic information. Finally, the Multi-Scale Fusion Unit (MSF) is used to guide the information merging of multiple stages, and the boundary information is improved by splicing the acquired deep multi-scale information with the information of the relevant feature extraction layer in the downsampling. The experimental results on several datasets show that the proposed approach outperforms other methodologies in segmentation accuracy.