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The traditional wheat area extraction methods mainly depend on artificial field investigation, which shows some disadvantages such as a big workload, low efficiency and high cost. Conversely, remote sensing technology has the advantages of high accuracy, rapid response and dynamic monitoring. It has become an effective measurement to extract crop areas. The Landsat-8 satellite remote sensing image of Zhengding County in Shijiazhuang was used as the training data, the image of Zengcun Town in Gaocheng District was used as test data. The GF-6 with resolution of 8m and Sentinel-2 with resolution of 10m were selected as comparative validation data. An improved U-Net was proposed to extract winter wheat planting areas. Landsat-8 was firstly preprocessed and the label set of wheat areas were marked and trained by using the U-Net network. The Squeeze and excitation (SE) attention mechanism module was introduced to better consider the information between feature channels, and the Batch normalization (BN) layer was used to suppress the over-fitting problem. The classification results were obtained through the Softmax classifier. SegNet, Deeplabv3+ and U-Net were selected as the comparison models and GF-6, Sentinel-2 and Landsat-8 data were used to construct the models, respectively. The results showed that the SE-UNet network performed best in the test data set based on Landsat-8 data prediction model, with the MPA and MIoU of 89.88% and 81.44%, respectively. This method can provide a reference for identifying large-scale winter wheat planting areas.