Abstract: Based on domestic GF-1 remote sensing imagery, taking the Darinor Wetland Nature Reserve as the research area, we extracted land cover remote sensing information by adopting object-oriented and traditional visual interpretation classification methods, and compared and analyzed the results, verified the accuracy of the object-oriented classification results by using the confusion matrix. The results showed that: (1) spectral information of GF-1 remote sensing images was fully utilized, and the object-oriented classification method with trial-and-error to determine the optimal segmentation scale of 550, and the shape and compactness factors were 0.6 and 0.5, and each band weight was 1; (2) the overall classification accuracy of object-oriented classification was up to 98.22%, and the KAPPA coefficient was about 0.96; (3) land cover information of relatively complex types was extracted by the object-oriented classification method quickly and accurately, which provided a reference for accurate and rapid extraction of land cover for the inland wetland to provide technical specifications for wetland remote sensing operational monitoring.