机载激光点云与影像联合测图的技术进展和趋势

本文通过对激光点云与光学影像联合测图所涵盖的关键技术进展进行分析,重点指出目前激光扫描仪进行测绘生产所面临的主要挑战。结合激光扫描技术与光学影像融合后的数据特点与实际行业生产需求,简要探讨了未来激光测图技术发展的趋势及新产品体系构建设想,以期对激光扫描软硬件系统在相关行业应用推广中具有一定参考意义。     

Comparative spatio-spectral heterogeneity analysis using multispectral and hyperspectral airborne images

Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible, near-infrared and short wave infrared spectra is important. Till now, little comparative research on spatio-spectral heterogeneity has been conducted on real multisensor images, especially on both multispectral and hyperspectral airborne images. In this study, four airborne images, Airborne Thematic Mapper, Compact Airborne Spectrographic Imager, Specim AISA Eagle and AISI Hawk hyperspectral airborne images of woodland and heath landscapes at Harwood, UK, were applied to quantify and evaluate the differences in spatial heterogeneity through semivariogram modelling. Results revealed that spatial heterogeneity of multisensor airborne images has a close relationship with spatial and spectral resolution and wavelength. Within the visible, near-infrared spectra and short wave infrared spectra, greater spatial heterogeneity is generally observed from the relatively longer wavelength in short wave infrared spectra. There are dramatic changes across the red and red edge spectra, and the peak value is generally examined in the red middle or red edge wavelength across the visible and near-infrared spectra for vegetation or non-vegetation landscape respectively. In all, for real multisensor airborne images, the change in spatial heterogeneity with spatial resolution will accord with the change of support theory depending on whether dramatic change exists across the corresponding wavelength. Besides, if with close spatial resolution, the spatial heterogeneity of multispectral images might be far from the overall integration of these bands from the hyperspectral images involved. A comparative assessment of spatio-spectral heterogeneity using real hyperspectral and multispectral airborne images provides practical guidance for designing the placement and width of a spectral band for different applications and also makes a contribution to the understanding of how to reconcile spatial patterns generated by multisensors.     

多传感器集成水电站监测系统的设计与实现

水电站安全监测部位众多,监测难度大,它要求在恶劣的环境持续稳定地检测出水电站微小的物理变化.本文提出采用智能测量机器人、GNSS天线阵列技术、测斜仪以及温度传感器等,设计多传感器集成的水电站一体化自动安全监测系统,并通过数据库实现监测数据的管理查询.     

An Overview of Passive and Active Dust Detection Methods Using Satellite Measurements

In this paper, the methods to detect dust based on passive and active measurements from satellites have been summarized. These include the visible and infrared (VIR) method, thermal infrared (TIR) method, microwave polarized index (MPI) method, active lidar-based method, and combined lidar and infrared measurement (CLIM) method. The VIR method can identify dust during daytime. Using measurements at wavelengths of 8.5, 11.0, and 12.0 μm, the TIR method can distinguish dust from other types of aerosols and cloud, and identify the occurrence of dust over bright surfaces and during night. Since neither the VIR nor the TIR method can penetrate ice clouds, they cannot detect dust beneath ice clouds. The MPI method, however, can identify about 85% of the dust beneath ice clouds. Meanwhile, the active lidar-based method, which uses the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data and five-dimensional probability distribution functions, can provide very high-resolution vertical profiles of dust aerosols. Nonetheless, as the signals from dense dust and thin clouds are similar in the CALIOP measurements, the lidar-based method may fail to distinguish between them, especially over dust source regions. To address this issue, the CLIM method was developed, which takes the advantages of both TIR measurements (to discriminate between ice cloud and dense dust layers) and lidar measurements (to detect thin dust and water cloud layers). The results obtained by using the new CLIM method show that the ratio of dust misclassification has been significantly reduced. Finally, a concept module for an integrated multi-satellites dust detection system was proposed to overcome some of the weaknesses inherent in the single-sensor dust detection.     

西昆仑山崇测冰川区多源遥感影像的冰川信息提取方法研究

冰雪独有的性质与特性使得基于遥感影像对其进行信息提取成为可能,如何进行精准的冰雪信息提取是冰雪时空变化研究的关键和基本要求。利用多源遥感影像（TM、IRS-P5和SAR）对西昆仑山崇测冰川区的冰川进行信息提取,采用不同分类方法和数据融合方法,分别针对光学影像和微波影像进行处理,提取冰川信息并进行比较分析。结果表明：面向对象分类方法是最优的冰川信息提取方法;图像融合处理有助于提高冰川信息的提取精度,特别是多光谱和高分辨率图像融合后再分类,提取效果更为理想。