Cross-view　geo-localization　of　satellite　and　unmanned　aerial　vehicles　(UAVs)　imagery　has　attracted　extensive　attention　due　to　its　tremendous　potential　for　global　navigation　satellite　system　(GNSS)　denied　navigation.　However,　inadequate　feature　representation　across　different　views　coupled　with　positional　shifts　and　distance-scale　uncertainty　are　key　challenges.　Most　of　the　existing　research　mainly　focused　on　extracting　comprehensive　and　fine-grained　information,　yet　effective　feature　representation　and　alignment　should　be　imposed　equal　importance.　In　this　article,　we　propose　an　innovative　transformer-based　pipeline　TransFG　for　robust　cross-view　image　matching,　which　incorporates　feature　aggregation　(FA)　and　gradient　guidance　(GG)　module.　TransFG　synergically　takes　advantage　of　FA　and　GG,　achieving　an　effective　balance　in　feature　representation　and　alignment.　Specifically,　the　proposed　FA　module　implicitly　learns　salient　features　and　dynamically　aggregates　contextual　features　from　the　vision　transformer　(ViT).　The　proposed　GG　module　uses　the　gradient　information　of　local　features　to　further　enhance　the　cross-view　feature　representation　and　aligns　specific　instances　across　different　views.　Extensive　experiments　demonstrate　that　our　pipeline　outperforms　existing　methods　in　cross-view　geo-localization.　It　achieves　an　impressive　improvement　in　R@1　and　AP　than　the　state-of-the-art　(SOTA)　methods.