Extraction of maize planting area based on transformer and remote sensing data

As one of the main food crops in China, timely and accurate monitoring of the planting area and acreage of corn is of great significance for the evaluation of agricultural productivity and ensuring food security.Based on ESA Sentinel-2 MSI remote sensing image data, the NDVI time series curves are extracted with the support of Google Earth Engine cloud platform, the transformer model is built, and the time series data are input into the model to obtain the typical feature classification results, and the maize planting areas in typical agricultural areas of North China Plain are extracted, and the accuracy is verified by using field survey data, and compared with convolutional neural network The results were compared with those of random forest classification and convolutional neural network. The results show that the overall accuracy of transformer classification is higher and the classification effect is better compared with random forest and convolutional neural network algorithms. Therefore, the use of transformer can effectively improve the crop planting area extraction accuracy.