Many　methods　have　been　developed　to　improve　the　performance　of　image　registration.　In　this　letter,　we　introduce　a　novel　method　based　on　a　local　affine　constraint　for　remote　sensing　image　registration,　which　can　be　widely　used　in　image　processing　and　pattern　recognition.　Our　algorithm　has　three　components.　First,　we　exploit　the　scale　invariant　feature　transform　(SIFT)　method　to　extract　feature　points　and　calculate　the　gradient　magnitude　to　establish　feature　descriptors　with　a　circular　instead　of　square　neighborhood.　Second,　an　initial　matching　is　implemented　by　the　nearest　neighbor　distance　ratio　(NNDR)　and　the　fast　sample　consensus　(FSC)　algorithm.　Finally,　fine　registration　is　established　using　more　correct　matches　obtained　by　the　local　affine　transformation　circular　region　search　algorithm.　Experimental　results　show　that　the　proposed　method　achieves　subpixel　accuracy.　In　addition,　both　the　correct　matching　rate　and　registration　demonstrate　the　effectiveness　and　efficiency　of　our　method.