Abstract: Based on GF-1 WFV and Landsat8 OLI remote sensing data, the planting area of Brassica campestris in Luoping was extracted by using the nearest neighbor classification and the maxium likelihood method. Based on the actual sampling points, a confusion matrix was constructed to verify the accuracy and the relative error of the planting area of rapeseed was compared. The results showed that whether GF-1 WFV or Landsat8 OLI, both methods had better extraction results. The object-oriented classification method can better avoid the problem of misclassification and leakage of mixed pixels in complex mountainous areas. It was superior to traditional pixel-based classification in terms of overall accuracy, Kappa coefficient, rapeseed producer accuracy and user accuracy, and it was more suitable for extracting feature information in the Karst Mountain area. Extracting the same data by different methods, the relative error of rape planting area was only 0.74% when using the nearest neighbour method to extract GF-1 WFV data, the accuracy was much higher than -7.37% of maximum likelihood method. For Landsat8 OLI data, the nearest neighbor method was also more accurate than the maximum likelihood method. Extracting the different data by same methods, the nearest neighbor method was more suitable for GF-1 WFV data with high spatial resolution, the maximum likelihood method was more suitable for Landsat8 OLI data with richer spectral information.