A　significant　strategy　for　achieving　a　balance　between　spatial　and　spectral　resolution　is　to　combine　hyperspectral　remote　sensing　images　with　low　spatial　resolution　and　multispectral　remote　sensing　images　with　high　spatial　resolution.　For　the　issues　of　spectrum　distortion　and　lack　of　the　ability　of　gaining　high　frequency　information　when　the　Fuvar　algorithm　employs　the　Vertex　Component　Analysis　(VCA)　technique,　this　thesis　presents　a　modified　Fuvar　(MFuvar)　algorithm　that　utilizes　the　Maximum　Distance　Analysis　(MDA)　method　instead　of　the　VCA　approach.　The　two　subsets　from　the　hyperspectral　remote　sensing　image　of　GF-5　and　the　multispectral　remote　sensing　image　of　Sentinel-2A,　representing　different　land　cover　types　were　used　as　test　data.　The　spectral　fidelity　and　the　ability　of　gaining　high　frequency　information　were　assessed　by　using　visual　and　statistical　analysis.　Fused　images　are　compared　with　eight　fusion　methods,　including　SFIMHS,　GLPHS,　MAPSMM,　CNMF,　Hysure,　SpaFusion,　LTTR,　and　Fuvar,　respectively.　The　results　show　that　the　MFuvar　algorithm　can　keep　the　best　balance　between　spectral　fidelity　and　the　ability　of　gaining　high　frequency　information,　and　it　is　generally　better　than　the　compared　eight　algorithms.　And　it　fulfils　the　automatic　selection　of　end　elements　without　manual　intervention　and　increases　the　efficiency　of　algorithm　operation.　©　2024　The　Author(s).　Published　by　Informa　UK　Limited,　trading　as　Taylor　&　Francis　Group.