Adaptive　sparse　representations　of　signals　have　drawn　considerable　interest　in　the　past　decade.　In　this　paper,　we　address　the　problem　of　training　dictionaries　for　massive　images　and　propose　a　new　algorithm　for　adapting　dictionaries　by　extending　the　classical　K-SVD　based　on　only　a　single　image.　The　approach　presented　in　this　paper　aims　at　training　the　adapting　dictionary　from　massive　samples,　other　dictionary　learning　methods　such　as　Online　Dictionary　Learning　(ODL)　and　Recursive　Least　Squares　Dictionary　Learning　Algorithm　(RLSDLA)　also　could　train　the　dictionary　by　using　relative　large　samples.　Our　method　is　competed　with　the　above　two　state-ofthe-art　dictionary　learning　methods.　Experiments　demonstrate　the　effectiveness　of　the　proposed　dictionary　learning　in　dealing　with　massive　spatial-temporal　remote　sensing.