To improve the accuracy and robustness of river edge extraction in remote sensing images, a river edge detection method based on an improved EDTER algorithm is proposed. Addressing issues such as edge detail loss and noise interference in complex natural scenes with traditional methods, a more adaptive edge detection scheme is introduced. The method employs a two-stage edge detection framework with Swin Transformer as the backbone network, incorporating a residual multilayer perceptron to enhance feature representation and designing a lightweight multi-scale decoder to improve detection efficiency. The first stage extracts global image features to generate a global edge map, while the second stage performs local feature extraction and fuses it with global features to produce the final edge detection result. Comparative experiments on the BSDS500 dataset and a self-constructed cross-regional river edge dataset demonstrate that the proposed method outperforms mainstream approaches in ODS, OIS, and AP metrics. Notably, the AP value on the cross-regional river edge dataset reaches 0.871, an improvement of approximately 3.2% over the EDTER method, indicating superior edge extraction capability in complex remote sensing scenarios. The proposed method effectively balances detection accuracy and computational efficiency, exhibiting strong generalization performance and suitability for water body edge extraction tasks in various remote sensing images, with significant engineering application value.