A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images

Abstract

Extraction of lake water bodies from remote sensing images provides reliable data support for water resource management, environmental protection, natural disaster early warning, and scientific research, and helps to promote sustainable development, protect the ecological environment and human health. With reference to the classical encoding-decoding semantic segmentation network, we propose the network model R50A3-LWBENet for lake water body extraction from remote sensing images based on ResNet50 and three attention mechanisms. R50A3-LWBENet model uses ResNet50 for feature extraction, also known as encoding, and squeeze and excitation (SE) block is added to the residual module, which highlights the deeper features of the water body part of the feature map during the down-sampling process, and also takes into account the importance of the feature map channels, which can better capture the multiscale relationship between pixels. After the feature extraction is completed, the convolutional block attention module (CBAM) is added to give the model a global adaptive perception capability and pay more attention to the water body part of the image. The feature map is up-sampled using bilinear interpolation, and the features at different levels are fused, a process also known as decoding, to finalize the extraction of the lake water body. Compared with U-Net, AU-Net, RU-Net, ARU-Net, SER34AUNet, and MU-Net, the R50A3-LWBENet model has the fastest convergence speed and the highest MIoU accuracy with a value of 97.6%, which is able to better combine global and local information, refine the edge contours of the lake’s water body, and have stronger feature extraction capability and segmentation performance.