Remote　sensing　technology　enables　us　to　monitor　the　Earth　from　space　and　sense　the　rhythm　of　rivers,　lakes,　and　seas　and　the　pulse　of　social　and　economic　development　in　real　time.　It　also　facilitates　effective　early　warning,　prevention,　and　evaluation　of　natural　disasters,　in　which　SAR　technology　plays　an　increasingly　important　role.　Remote　sensing　image　classification　is　an　important　step　of　remote　sensing　image　analysis,　and　it　has　always　been　one　of　the　hot　spots　in　related　research　fields.　Owing　to　the　complexity　of　ground　target　characteristics　and　the　diversity　of　remote　sensing　imaging　techniques,　the　accurate　interpretation　of　remote　sensing　images　requires　a　deep　understanding　of　the　characteristics　of　the　image　and　fully　utilizing　the　prior　knowledge　of　ground　objects.　In　recent　years,　the　development　of　Synthetic　Aperture　Radar　(SAR),　especially　polarimetric　SAR　technology,　has　facilitated　the　rapid　growth　in　the　research　on　remote　sensing　object　classification.　In　this　study,　the　research　progress　of　polarimetric　SAR　remote　sensing　image　classification　is　reviewed.　This　study　firstly　introduces　the　basic　theory　of　SAR　remote　sensing　and　the　main　data　sources　of　spaceborne　SAR.　Then,　it　introduces　the　decomposition　of　polarimetric　SAR　data,　the　classical　machine　learning　algorithms　for　polarimetric　SAR,　the　deep　learning-based　algorithms,　the　methods　of　fusing　optical　and　SAR　images,　and　the　classification　algorithms　based　on　compact　polarimetric　SAR.　Next,　this　study　introduces　the　research　progress　of　polarimetric　SAR　image　classification　for　marine　oil　spill　detection,　ship　detection,　coastline　extraction,　land　use　classification,　and　sea　ice/ice　cap　classification.　Finally,　the　development　trend　of　polarimetric　SAR　image　classification　is　prospected.　From　the　perspective　of　the　authors,　the　development　of　polarimetric　SAR　classification　has　the　following　trends:　(1)　from　single　polarimetric　to　multi-　and　compact　polarimetric　SAR　modes;　(2)　from　medium/low　resolution,　small　range　to　high　resolution,　large　range　remote　sensing　applications;　(3)　from　single　temporal　to　multiple　temporal　sequence　image　analysis　applications;　(4)　from　manual　design　of　feature　extraction　methods　to　automatic　feature　extraction　using　deep　learning　models;　(5)　from　single-source　SAR　image　classification　to　SAR,　optical,　LiDAR,　and　other　multi-source　image　fusion　classification.　The　key　technologies　of　radar　signal　processing,　image　analysis,　pattern　recognition,　multi-source　information　fusion,　big　data　analysis,　and　other　aspects　need　to　be　understood　to　fully　utilize　the　information　provided　by　polarimetric　SAR　data　sources.　The　rapid　development　of　technology　requires　talents　with　interdisciplinary　backgrounds　such　as　electronic　engineering,　remote　sensing,　and　artificial　intelligence　in　this　field.　The　authors　hope　that　through　the　introduction　of　this　article,　readers　can　improve　their　understanding　of　the　field　of　SAR　remote　sensing　classification　to　a　certain　extent　for　better　grasping　the　development　trends　of　this　technology.　©　2024　Science　Press.　All　rights　reserved.