Change　detection　is　an　important　task　of　identifying　changed　information　by　comparing　bitemporal　images　over　the　same　geographical　area.　Currently,　many　existing　methods　based　on　U-Net　and　attention　mechanism　have　greatly　promoted　the　development　of　change　detection　techniques.　However,　they　still　suffer　from　two　main　challenges.　First,　faced　with　the　diversity　of　ground　objects　and　the　flexibility　of　scale　changes,　vanilla　attention　mechanisms　cripple　spatial　flexibility　in　learning　object　details　due　to　the　same　scale　convolution　kernels　at　different　convolution　layers.　Second,　the　complex　background　and　high　similarity　between　changed　information　and　nonchanged　information　makes　it　difficult　to　fuse　low-level　details　and　high-level　semantic　by　simple　skip-connection　in　U-Net.　To　address　the　above　issues,　a　local　and　global　feature　learning　with　kernel　scale-adaptive　attention　network　(LGSAA-Net)　is　proposed　in　this　article.　The　proposed　network　makes　two　contributions.　First,　a　scale-adaptive　attention　(SAA)　module　has　been　designed　to　exploit　the　relationships　between　feature　maps　and　convolutional　kernel　scales.　The　SAA　module　can　achieve　better　feature　discrimination　than　vanilla　attention　mechanism.　Second,　a　multilayer　perceptron　based　on　patches　embedding　has　been　employed　by　skip-connection　to　learn　the　local　and　global　pixel　association,　which　is　helpful　for　achieving　globally　deep　fusion　of　low-level　details　and　high-level　semantics.　Finally,　experiments　and　ablation　studies　are　conducted　on　three　datasets　of　LEVIR/WHU/GZ.　Experimental　results　demonstrate　that　the　proposed　LGSAA-Net　performs　favorably　against　comparative　current　approaches　and　provides　more　accurate　contour　and　better　internal　compactness　for　changed　targets,　thus　verifying　the　effectiveness　and　superiority　of　the　proposed　LGSAA-Net　in　VHR　remote　sensing　change　detection.