In　high-resolution　remote　sensing　image　processing,　segmentation　is　a　crucial　step　that　extracts　information　within　the　object-based　image　analysis　framework.　Because　of　its　robustness,　mean-shift　segmentation　algorithms　are　widely　used　in　the　field　of　image　segmentation.　However,　the　traditional　implementation　of　these　methods　cannot　process　large　volumes　of　images　rapidly　under　limited　computing　resources.　Currently,　parallel　computing　models　are　generally　employed　for　segmentation　tasks　with　massive　remote　sensing　images.　This　paper　presents　a　parallel　implementation　of　the　mean-shift　segmentation　algorithm　based　on　an　analysis　of　the　principle　and　characteristics　of　this　technique.　To　avoid　the　inconsistency　on　the　boundaries　of　adjacent　data　chunks,　we　propose　a　novel　buffer-zone-based　data-partitioning　strategy.　Employing　the　proposed　data-partitioning　strategy,　two　intensively　computation　steps　are　performed　in　parallel　on　different　data　chunks.　The　experimental　results　show　that　the　proposed　algorithm　effectively　improves　the　computing　efficiency　of　image　segmentation　in　a　parallel　computing　environment.　Furthermore,　they　demonstrate　the　practicality　of　massive　image　segmentation　when　computer　resources　are　limited.