Abstract: To achieve the crop automatic classification based on remote sensing images and take advantage of the characteristics of macro, rapid and large scale monitoring, based on the remote sensing image unsupervised classification, an automatic classification method on the basis of ISODATA unsupervised classification was provided. The method had ISODATA unsupervised classification process and automatic classification process. The automatic classification process included the proportion ranking of winter wheat sample and winter wheat identification. When the classification categories of unsupervised classification were set to be 40 or 50 respectively, and each category had 4 or 5 samples, the winter wheat classification accuracy was relatively high and stable. Among the 200 sample point combinations (40 classified categories, each category with 5 sample points), the overall accuracy of winter wheat classification based on automatic ISODATA unsupervised classification method was 2.5 percent higher than that of the maximum likelihood classification method and its KAPPA coefficient rose by 19.4%. Among 500 sample point combinations (100 classified categories, each category with 5 sample points), the winter wheat overall accuracy and its KAPPA coefficient based on automatic ISODATA unsupervised classification method were close to that of the maximum likelihood classification method. The automatic ISODATA unsupervised classification method has relatively higher classification accuracies with fewer samples, and it is featured with less human- machine interaction and higher classification efficiency. Therefore, this method is suitable for operational applications.