摘要
机载LiDAR点云系统由于获取三维立体信息方便、快捷,已被广泛应用到城区目标的提取与识别中,但LiDAR点云数据缺乏光谱特征,对建筑物提取识别时常在植被茂密的树冠处出现错检现象。针对这一问题,提出了融合航空影像光谱特征与LiDAR点云几何特征的建筑物提取算法。通过LiDAR点云数据与航空影像数据的配准,实现了点云数据光谱信息的提取;通过改进传统的张量投票机制,融合光谱特征与空间几何特征形成了新的融合分类特征;运用随机森林算法实现了建筑物点的提取。仿真实验基于ISPRS提供的测试数据集进行,通过对比融合光谱特征前后的实验结果可知,所提算法的精度明显提高,提取质量达到94.26%,证明了融合光谱特征对于建筑物提取精度提升的重要作用。
Airborne LiDAR systems are widely used in urban objects extraction and recognition because of the advantages in obtaining 3D information conveniently and rapidly. However, it considers geometrical features regardless of buildings and vegetation spectral features and error rate is high in the dense canopy. Aiming at this problem, an algorithm of building extraction by fusing spectral features in aerial images and geometrical features in LiDAR data is proposed. Firstly, the spectrum information can be obtained by registering with LiDAR data. Then, the new feature which fuses spectral and geometrical information is formed by improved tensor voting. Finally, building extraction is achieved by random forests algorithm. Simulation test datasets are provided by ISPRS. Through the comparison of results before and after fusing spectral features, the accuracy of the proposed algorithm is obviously high and the extraction quality of proposed algorithm reaches to 94.26%. The simulation results prove the importance of fusing spectral features in building extraction.
作者
何曼芸
程英蕾
廖湘江
赵中阳
He Manyun;Cheng YingleP;Liao Xiangjiang;Zhao Zhongyang(College of Information and Navigation, Air Force Engineering University, Xi＇an, Shaanxi 710077, China;Unit of 94816, Fuzhou, Fujian 350002, China)
出处
《激光与光电子学进展》
CSCD
北大核心
2018年第4期374-381,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(41601436)
陕西省自然科学基金(2015JM6346)
