Vegetation　is　a　land-use　type　that　attracting　growing　attention　in　urban　studies.　Extraction　of　urban　vegetation　information　can　be　achieved　by　land-use/land-cover　(LULC)　classification.　It　has　been　proved　that　SAR　remote　sensing　provides　complementary　information　in　LULC　classification.　However,　how　the　complementary　information　from　SAR　features　contribute　to　the　classification　in　highly-dense　urban　area　is　still　unclear.　In　this　study,　taking　the　highly　urbanized　area　of　Hong　Kong　as　the　study　site,　comprehensive　studies　were　conducted　to　investigate　the　contribution　of　optical　and　polarimetric　SAR　features　for　LULC　classification　and　urban　vegetation　extraction.　Firstly,　large　amount　of　redundant　information　was　found　within　optical　and　SAR　features　sets　by　Pearson　correlation　analysis.　Then　analysis　based　on　J-S　distance　revealed　that　optical　features　have　much　stronger　capabilities　on　LULC　classification　and　some　key　features　such　as　NDVI　and　Mean　value　of　GLCM　carry　distinct　information　for　discriminating　certain　land-use　types.　Supervised　classification　based　on　random　forest　further　proved　this　result.　The　complementary　mechanism　of　optical　and　polarimetric　SAR　features　can　be　clearly　observed　in　the　radar　chart.　Therefore,　when　fusing　optical　and　SAR　features　on　the　feature-level,　the　classification　accuracy　further　improved.　It　was　discovered　that　the　volume　scatter　mechanism　plays　a　vital　role　in　the　feature-level　classification　model.　Finally,　the　overall　classification　accuracy　of　the　classification　model　does　not　significantly　grow　after　several　key　optical　and　SAR　features　are　inputted,　confirming　again　the　high　correlation　between　the　features.　In　vegetation　extraction,　optical　features　provide　most　information　and　achieved　the　high　accuracy　of　99.02%.　The　results　discovered　in　the　above　research　could　benefit　the　designing　algorithms　and　selecting　features　for　high　accuracy　urban　LULC　classification　and　vegetation　extraction　based　on　optical　and　SAR　data.