Currently, deep convolutional neural networks have made great progress in the field of semantic segmentation. Because of the fixed convolution kel geometry, standard convolution neural networks have been limited the ability to simulate geometric transformations. Therefore, a deformable convolution is introduced to enhance the adaptability of convolutional networks to spatial transformation. Considering that the deep convolutional neural networks cannot adequately segment the local objects at the output layer due to using the pooling layers in neural network architecture. To overcome this shortcoming, the rough prediction segmentation results of the neural network output layer will be processed by fully connected conditional random fields to improve the ability of image segmentation. The proposed method can easily be trained by end-to-end using standard backpropagation algorithms. Finally, the proposed method is tested on the ISPRS dataset. The results show that the proposed method can effectively overcome the influence of the complex structure of the segmentation object and obtain state-of-the-art accuracy on the ISPRS Vaihingen 2D semantic labeling dataset.