Infrared　ship　target　segmentation　is　one　of　the　key　technologies　for　automatically　detecting　ship　targets　in　ocean　monitoring.　However,　it　is　a　challenging　work　to　achieve　accurate　target　segmentation　from　the　infrared　ship　image.　To　improve　its　segmentation　performance,　we　present　an　Adversarial　Domain　Adaptation　Network　(ADANet)　for　infrared　ship　target　segmentation,　where　the　labeled　visible　ship　images　are　used　as　the　source　domain　and　the　unlabeled　infrared　ship　images　are　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　discriminator,　we　design　a　discriminator　based　on　atrous　convolution　to　improve　its　discriminative　ability.　Finally,　for　the　issue　of　low　confidence　of　the　target　domain　predicted　images,　we　add　the　information　entropy　of　the　target　domain　predicted　images　to　the　adversarial　loss.　Experimental　results　on　the　home-made　dataset　as　well　as　a　public　dataset　show　that　infrared　ship　target　segmentation　achieves　higher　mean　intersection　over　union　than　the　state-of-the-art　methods　without　significantly　increase　of　parameters,　demonstrating　its　effectiveness.(c)　2023　Elsevier　B.V.　All　rights　reserved.