Change　detection　in　SAR　images　is　an　important　but　challenge　task.　Due　to　the　difficulty　of　SAR　interpretation,　reliable　training　samples　are　lacking,　limiting　the　application　of　deep　learning　technology　in　SAR　image　change　detection.　To　overcome　this　problem,　this　article　proposes　an　unsupervised　SAR　image　change　detection　method　based　on　slow　feature　analysis　theory　with　convolutional　neural　network　(SAR-SFAnet).　It　adopts　SDAEs　to　automatically　extract　features　from　SAR　data,　and　employs　slow　feature　analysis　theory　to　project　the　extracted　multi-dimensional　features　into　a　new　space.　In　addition,　an　alternative　optimization　strategy　is　introduced,　making　the　features　learned　by　bi-temporal　stacked　denoising　auto-encoder　(SDAEs)　have　more　consistent　representations,　as　well　as　making　the　change　detection　map　more　accurate.　Finally,　comparative　experiments　are　carried　out　on　two　real　SAR　data　sets,　demonstrating　the　effectiveness　of　the　proposed　method.　©2021　IEEE