Feature　matching　is　a　fundamental　problem　in　feature-based　remote　sensing　image　registration.　Due　to　the　ground　relief　variations　and　imaging　viewpoint　changes,　remote　sensing　images　often　involve　local　distortions,　leading　to　difficulties　in　high-accuracy　image　registration.　To　address　this　issue,　in　this　article,　we　propose　a　robust　feature　matching　method　called　First　Neighbor　Relation　Guided　(FNRG)　for　remote　sensing　image　registration　via　guided　hyperplane　fitting.　The　key　idea　of　FNRG　is　to　exploit　the　first　neighbor　relation　of　feature　points　between　two　images　for　seeking　consistent　seeds　in　a　parameter-free　manner.　To　boost　more　consistent　matches　based　on　the　consistent　seeds,　we　formulate　the　feature　matching　problem　into　an　affine　hyperplane　fitting　problem　by　imposing　the　motion　consistency,　and　then　we　design　a　hyperplane　updating　strategy　to　refine　the　fitting　model.　We　also　introduce　a　locality　preserving　structure-based　cost　function　to　promote　the　matching　performance　of　the　hyperplane　updating　strategy.　Our　method　can　mine　consistent　matches　from　thousands　of　putative　ones　within　only　a　few　milliseconds,　and　it　also　can　handle　the　data　with　a　large-scale　change,　rotation,　or　severe　nonrigid　deformation.　Extensive　experiments　on　the　remote　sensing　image　data　sets　with　different　types　of　image　transformations　show　that　the　proposed　method　achieves　significant　superiority　over　several　state-of-the-art　methods.