基于激光雷达扫描的密集城区建筑物自动检测

Automatic detection of dense urban buildings based on lidar scanning

为改善城区建筑物自动检测的准确性,结合激光雷达扫描与光学成像提出一种新的城区建筑物自动检测技术。将激光雷达扫描产生的点云送入自编码器进行下采样与特征提取,将光学图像像素送入自编码器进行空间特征提取,构建特征图模型来融合不同的特征集。然后,设计了基于卷积神经网络的半监督分类器,识别城区的建筑物、地面以及绿植等不同区域。实验结果表明,所提技术可准确地检测城区的建筑物区域,为建设智慧城市提供基础。

In order to improve the automatic detection accuracy of urban buildings,a new automatic detection technique of urban buildings is proposed with combination of laser radar scanning and optical imaging technique.The proposed technique delivers the point cloud generated by laser radar scanning to the auto-encoder to down sampling and extract features,and the optical image pixels are delivered to auto-encoder to extract the spatial features,different feature sets are fused based on the graph model of features.Then,a semi-supervised classifier is designed based on convolutional neural network,the classifier is used to recognize different regions of buildings,ground and vegetation.Experimental results show that the proposed technique can detect the regions of urban buildings accurately,it also provides fundamental basis for smart cities development.