Aiming at the problem of holes and inaccurate edges in traditional remote sensing image residential area segmentation

a new generative adversarial network（ GAN） segmentation method is proposed based on traditional filtering method and full convolutional network method. Firstly

a full convolutional network is constructed as the generation network（ G） for learning the distribution rule of the remote sensing image residential area

and generate the residential area segmentation result map（ fake）; then

a convolution neural network classifier is designed as a discriminant network（ D） to distinguish the generated network segmentation result map（ fake） from the manual result map（ label）. G tries to deceive the D network. D distinguishes the fake image as much as possible and propagates error to train G network. Through adversarial training

the fake map generated by G is as similar as possible to the label map. Thereby it can obtain better segmentation results. The experimental results show that the method based on GAN network has better robustness in the remote sensing image residential area segmentation than Gabor filtering method and fcn method.