A fully convolutional network (FCN) model based on U-Net is proposed to implement the semantic segmentation of remote sensing images with high resolution, in which the data standardization and data augmentation are adopted for data preprocessing. In addition, the Adam optimizer is used for the model training and the average Jaccard index is used as the evaluation metric. A weighted cross entropy loss function and an adaptive threshold algorithm are employed to improve the classification accuracy of small classes. The experimental results on the DSTL dataset show that the proposed method can increase the average Jaccard index of prediction results from 0.611 to 0.636, and produces an accurate end-to-end classification for high-resolution remote sensing images.