Accurately mastering the planting area and distribution of the seed maize field can provide more accurate data for regulatory authorities, and timely detection of illegal seed production areas. According to the differences of high temporal phase spectrum, high spatial texture and shape of features, the identification of maize seed production field was carried out based on 163 ground samples, multisource sequential optimization of vegetation index set and texture analysis of high spatial resolution remote sensing images. Through correlation analysis, six vegetation indices (VIs) of the normalized difference vegetation index (NDVI), enhance vegetable index (EVI), normalized difference water index (NDWI), triangle vegetation index (TVI), ratio vegetable index (RVI) and difference vegetation index (DVI) were identified from eight VIs reflecting different growth conditions of vegetation. And the random forest (RF) classification algorithm was used to identify the seed maize field. The graylevel co-occurrence matrix (GLCM) texture feature system was constructed by using the 0.7m Kompsat-3 image of tasseling stage. It contained five texture features: mean, entropy, contrast, angular second moment (ASM) and homogeneity. At the same time, Subtract texture features were proposed in order to reflect the characteristics of the intercropping of corn parents. Before constructing the texture feature system, local binary patterns (LBP) processing on the image was performed to solve the directional problem of crop planting texture in the image. The random forest was used to identify the seed maize field from maize field classification results. Qitai County in Xinjiang Uyghur Autonomous Region was taken as a research area to verify the proposed method, the results showed that the user’s accuracy and mapping accuracy of the seed maize field was 99.19% and 93.34%, respectively. The research result can provide further technical support for the monitoring and supervision of hybrid corn farming in China.