The extraction of small water bodies in the Yellow River Basin has always been a key issue of concern in the fields of remote sensing technology application, water resource management, environmental science, and geographic information systems. Due to factors such as water bodies, human activities, and cloud cover, water body extraction becomes difficult. In addition, convolutional neural networks are prone to losing small water body feature information during the process of extracting local features, which can cause more imbalance between positive and negative samples of water bodies and non-water bodies. In response to these issues, this study focused on a specific research area—the middle and lower reaches of the Yellow River. We processed and analyzed high-resolution optical satellite images collected from the Yellow River Basin and other areas, with a particular emphasis on precise identification of small water bodies, and proposed a network structure, the SE-Attention-Residual-Unet (SE-ResUnet), for water extraction tasks.The main contributions of this article are threefold: (1) Introducing a channel attention mechanism with residual structure in the down-sampling process, and learning Unet’s skipping structure for multi-scale feature extraction and compensation, thereby enhancing the feature extraction ability of small water bodies, including rivers, lakes, and reservoirs. (2) Introducing a weighted-Dice (W-Dice) loss function to balance positive and negative samples and enhance the generalization of the model. (3) In comparative experiments on improving the Unet model with semantic segmentation networks such as Unet, PSPNet, Deeplabv3+ on a self-built dataset and remote sensing interpretation public dataset, excellent performance and results were achieved on the mIoU, OA, and F1-score metrics. On the self-built dataset, compared with Unet, the mIoU, OA, and F1-score improved by 0.38%, 0.12%, and 0.08%, respectively. On the publicly available dataset, for remote sensing interpretation of water extraction, the mIoU, OA, and F1-score improved by 0.63%, 0.26%, and 0.25%, respectively. The experimental results demonstrate that a strategy combining an attention mechanism and a weighted loss function has a significant effect on the effectiveness of the collaborative improvement of neural network models in water extraction tasks.
Read full abstract