In　order　to　extract　buildings　using　only　gray　information,　this　article　proposed　an　approach　for　recognizing　and　extracting　buildings　from　panchromatic　high-resolution　remotely　sensed　imagery　based　on　shadows　and　segmentation.　First,　shadows　were　detected　by　potential　histogram　function.　Second,　the　value　of　neighborhood　total　variation　for　each　pixel　was　calculated,　and　then　binarization　and　annotation　were　implemented　to　generate　lable　regions　whose　centroids　were　used　as　the　seeds　of　the　region　growing　segmentation,　candidate　buildings　were　selected　from　the　segmentation　result　with　the　constraint　of　aspect　ratio　and　rectangularity.　At　last,　shadows　were　processed　with　open,　dilate　and　corrode　operations　respectively,　buildings　were　extracted　by　computing　the　adjacency　relationship　of　the　processed　shadows　and　candidate　buildings,　and　the　building　boundaries　were　fitted　with　the　minimum　enclosing　rectangle.　For　verifying　the　validity　of　the　proposed　method,　eighteen　representative　sub-images　were　chosen　from　PLEIADES　images　covering　Shenzhen,　China.　Experimental　results　show　that　the　average　precision　and　recall　of　the　proposed　method　are　97.95　%　and　79.40　%　for　the　object-based　evaluation,　and　are　98.75　%　and　83.16　%　for　the　area-based　evaluation　respectively,　and　it　has　more　10　%　and　6　%　increase　in　the　overall　performance　for　above　two　evaluation　criterion　comparing　with　two　other　similar　methods.