A novel method for the cloud detection of ZY-3 satellite remote sensing images is proposed based on the fully convolutional neural network (FCN) combined with the conditional random field. The model of a fully convolutional neural network is optimized and the FCN after three times of upsampling (FCN-8s) is upsampled. The momentum combined adaptive algorithm is used for the acceleration of convergence by adjusting the learning rate of parameters. The fully convolutional neural network is combined with the conditional random field, the fully convolutional output image is taken as the first-order potential of the front end, and the Gaussian kernel function is used as the second-order potential of the back end. The mean-shift regional constraints are added to protect the local feature information of images and the posterior probability of the conditional random field model is inferred by the mean field algorithm. The experimental results show that the proposed cloud detection method can increase the identification accuracy rate of an image cloud region to 97.38%, which is 13.42% higher than that from FCN-8s.