Multi-aspect　polarimetric　SAR　image　set　is　rich　of　anisotropic　scattering　signatures　and　beneficial　for　terrain　classification　by　using　a　series　of　implicit　aspect-polarimetry-signature　sequence.　As　the　sequence　information　is　hidden　in　the　multi-aspect　polarimetric　sub　aperture　image　set　and　difficult　to　use.　An　anisotropic　scattering　detection　model　based　on　the　difference　of　polarization　distribution　can　help　to　extract　the　pixel-wise　aspect-signature　sequence　corresponding　to　one　or　more　estimated　scattering　centers,　and　simultaneously　consider　the　speckle　reduction.　Based　on　the　anisotropic　scattering　detection,　we　propose　a　new　merging　framework　combining　aspect　signatures　with　polarimetric　decomposition　signatures　in　this　paper.　First　of　all,　the　anisotropic　scattering　centers　are　detected　to　generate　a　new　sequence　images　from　the　original　sub-aperture　image　sequence.　Dozens　of　polarimetric　decomposition　model　and　anisotropic　scattering　model　components　are　performed　on　the　new　aspect-signature　sequence　images,　and　then　to　generate　a　batch　of　polarimetric　signature　vectors　(PSVs)　of　each　target.　There　are　two　methods　to　make　use　of　PSVs　in　terrain　classification　and　segmentation.　One　is　to　code　the　PSVs　which　represent　each　target’s　aspect-polarimetry-signature　first　and　then　to　classify,　the　other　is　to　classify　the　original　PSVs　directly.　Various　machine　learning　methods,　such　as　support　vector　machine　(SVM)　and　Markov　random　field　(MRF)　model,　have　been　investigated　for　validation　and　comparison　by　using　a　set　of　IECAS　airborne　P-band　fully　polarimetric　circular　SAR　data.　©VDE　VERLAG　GMBH　.　Berlin　.　Offenbach