Airborne laser radar (LiDAR) system can directly and effectively obtain three-dimensional point cloud information of ground features, to provide powerful data guarantee for the generation of digital elevation model, building detection and three-dimensional reconstruction. However, the original point cloud data will inevitably produce noise points. A method of noise removal for airborne LiDAR point cloud based on the multi-window top-hat transformation is proposed. The grid interpolation is performed on the point cloud according to the interval of point cloud to obtain the maximum and minimum grid data, respectively. The grid data is clustered, and the original noise areas are detected by setting the area size threshold. The maximum and minimum grids are processed using the white and black top-hat transformation theory respectively to detect the final grid area where noise points are located. The method is compared and analyzed with other methods based on the ISPRS data. The results show that the proposed method can remove the noise points, and completely preserve the details of the original point cloud.