To improve the segmentation accuracy of infrared ship target， we present an adversarial domain adaptation network for infrared ship target segmentation （ISADA）， where the labeled visible ship images are used as the source domain and the unlabeled infrared ship images as the target domain. To address the issue of style difference between the two domains， we preprocess the visible images of the source domain in turn with graying and whitening to convert them into the images with the style of the target domain. For the infrared images in the target domain， we optimize them with a denoising network. Furthermore， to solve the matter of limited receptive field of the discriminative network， we design a discriminative network based on atrous convolution. Finally， for the problem of low confidence of the target domain prediction images， the information entropy of the target domain prediction images is added to the adversarial loss. The experimental results on the datasets composed of visible and infrared ship images is superior than the state-of-the-art methods， which demonstrates the effectiveness of the proposed method.