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It is well know that geometric filters for points cloud can only go so far when removing above-ground phenomena for it's difficult to determine whether a laser point has hit a special object when only spatial analysis is included. And comparing to the discrete points cloud, the high quality, large-coverage images provided by aerial cameras is a very important advantage of photogrammetry, which can be a very important complement data source to the points cloud. And by a process of spectral imagery LIDAR composite, points cloud can be fused with accurate spectral images provided by aerial CCD cameras on the same board. And the points cloud, with both high quality of reflection and geometric properties, can be filtered by integrating the reflectivity and geometric method. In this paper, the measurement characteristics and advantages of reflectivity of laser scanning and CCD cameras for the classification of return points are analyzed, and a building extraction method, integrating the geometric feature and reflectivity information of the return's intensity and the spectral range of CCD camera are presented. In which, the vegetation points are filtered by spectral attributes initially, and then points belonged to the building walls are segmented by a area attributes after constructing the return points' voronoi diagram; and building surface points are filtered by plane-fitting clustering method.