Spatial　injection-based　pansharpening　methods　are　prone　to　spatial　or　spectral　distortions　in　pansharpening　images　due　to　insufficient　extraction　of　spatial　details　and　a　mismatch　between　the　amount　of　spatial　detail　information　injected　and　the　required　amount.　To　this　end,　this　paper　proposes　a　pansharpening　method　that　optimizes　spatial　detail　extraction　and　injection.　Firstly,　a　method　to　optimize　the　amount　of　spatial　detail　injection　is　proposed,　that　is,　to　extract　the　high-frequency　information　of　the　image　through　iterative　filtering　and　determine　the　optimal　number　of　iterations　based　on　the　global　analysis　of　the　method.　Then,　to　fully　extract　and　combine　the　spatial　detail　information　of　the　source　image,　the　detailed　high-frequency　image　extracted　corresponding　to　the　optimal　iterative　filtering　times　is　decomposed　by　non-subsampled　shearlet　transform　(NSST),　and　a　new　multi-scale　sum-modified-Laplacian　(NSML)　as　an　external　stimulus　to　a　parameter-adaptive　pulse-coupled　neural　network　model　(PAPCNN).　A　fusion　rule　based　on　multi-scale　morphological　gradients　is　designed　to　extract　a　small　amount　of　detailed　information　for　the　low-frequency　subband.　The　fused　spatial　detail　image　can　be　obtained　by　combining　the　fused　low-frequency　and　high-frequency　subbands　and　inverse　NSST　transformation.　Finally,　pansharpening　can　be　realized　by　combining　spatial　detail　image,　injection　coefficient,　and　MS　image.　In　this　paper,　many　experiments　are　carried　out　on　the　QuickBird,　GeoEye-1,　and　WorldView-4　datasets,　and　quantitative　and　qualitative　comparisons　are　made　with　eight　advanced　methods.　Experimental　results　show　that　the　method　proposed　in　this　paper　can　achieve　better　fusion　results.