The Yellow River Delta exhibits irregular tidal flat, with tidal creeks that vary in width and experience tidal creek current anisotropy. Given such characteristics, the GF-2 multi-spectral image was selected as the data source to characterize the details of tidal creeks. First, the normali-zed difference water index (NDWI) and OTSU classification were used to delineate the wide tidal creeks. Second, the modified fuzzy C-means clustering algorithm (MFCM) and multi-scale Gaussian matching filter (MGMF) were used to enhance the narrow tidal creeks on the basis of weakening the heterogeneity of tidal flat background. Then, the adaptive threshold segmentation was conducted to delineate the narrow tidal creeks. Finally, the complete tidal creek networks were delineated by combining the wide and narrow tidal creeks. We fully used the spatial resolution and spectral information of the GF-2 image and took into account the geometric features of the linear features, ensuring the spatial continuity of the tidal creek extraction results. In the four tested areas, the Kappa coefficient was greater than 0.8 and the overall accuracy was greater than 97%, which performed better than the maximum likelihood method and support vector machine. The results showed that the proposed method could completely differentiate different types of tidal creeks, with good extraction accuracy and stability. The method could provide scientific reference for real-time dynamic monitoring of tidal creek and its development and evolution.