Automatic building extraction from high spatial resolution remotely sensed imagery can accelerate the update process for urban basic geographic database.One problem of building extraction methods is the difficulty of extracting the precise building contour.This article proposes an approach to recognizing and extracting buildings from high resolution remotely sensed imagery based on shadows and graph-cut segmentation.Firstly

shadows were detected by using potential histogram function.Then

candidate segmentation objects were selected from the result of graph-cut segmentation with the constraint by integrating aspect ratio and rectangularity.At last

shadows were processed with open

dilate and corrode operations respectively

while buildings and their exact boundaries were extracted with adjacency between processed shadows and candidate segmentation objects.For verifying the validity of the proposed method

six sub-images were chosen from PLEIADES images.Experimental results show that the average precision and recall of the proposed method are 92.31% and 74.23% respectively.