Image　segmentation　is　a　valuable　approach　that　performs　an　object-based　rather　than　a　pixel-based　analysis　of　high-resolution　remotely　sensed　imagery.　An　approach　for　segmenting　the　multispectral　QuickBird　image　based　on　first　fundamental　form　is　proposed.　The　value　of　multispectral　image　at　a　given　point　can　be　regarded　as　N-dimensional　vector,　and　the　difference　of　image　values　can　be　defined　from　the　theory　of　first　fundamental　form,　which　allows　to　access　gradient　information　from　all　bands　simultaneously.　Thus　it　is　used　to　fuse　the　gradient　feature　of　all　bands.　Then,　the　image　segmentation　is　implemented　based　on　marker-controlled　watershed　transform.　The　experimental　results　show　that　the　proposed　approach　gives　a　better　solution　of　integrating　multispectral　information　for　the　segmentation　of　remotely　sensed　imagery.　©2009　IEEE.