Abstract: In order to improve the precision of remote sensing image for crop prediction and the efficiency of agricultural planting, combined with the innovation and entrepreneurship training program of Jilin University, an experimental project of crop classification algorithm based on satellite remote sensing image is designed. Taking the high-resolution satellite image of Harbin agricultural demonstration base captured by sentinel-2 on July 30, 2018 as the experimental data, the characteristics of rice, soybean, corn and sorghum in the image are extracted and classified by using the maximum likelihood method, support vector machine method and neural network method in different spectral bands ( including red band) , and then the crop classification map is obtained, the statistical results are compared with the real parameters, and then the classification accuracy and reliability of different algorithms are compared. The experimental results show that the neural network method has the highest accuracy and the strongest reliability, and is suitable for nationwide promotion.

Key words: satellite remote sensing, red edge band, neural network, crop classification