Abstract: 【Objective】It can provide a reference for future large-scale crop remote sensing recognition method. 【Methods】We took the Sentinel_2A images as the main source of Arun Banner in Inner Mongolia. Based on a random forest method and the full utilization of the spectral features,texture features,and parameter features to extract the area of plants including corn,sorghum,soybean,and sugar,the classification results were analzyed. 【Results】The overall accuracy of the crop classification reached 80.02%,the Kappa coefficient achieved 0.727 7,and the classification results are good. 【Conclusion】Using random forest as a classifier combined with the way of mean shift it can ameliorate the phenomenon of “salt and pepper” and improve the accuracy of classification of crops.

Key words: Random forest method, Mean shift, Crop classification, Arun Banner