Satellite laser range system measures the distance between the satellite and the surface of the earth by figuring out the transit time of laser pulse. The beam is refracted when it goes through the atmosphere. The atmosphere refraction effect causes laser propagation delay and path bending, which is one of the key factors to restrict the accuracy of laser ranging. In order to improve the accuracy of atmospheric refraction delay correction, it is necessary to strengthen the study of atmospheric group refractivity models and atmospheric refraction delay correction method. According to the datum of Xuzhou upper air meteorological station, which are the monthly values of upper limit layers for 30 years (1981-2010) in China, three atmospheric group refractivity models were analyzed and compared. The atmospheric refraction delays to LiDAR were calculated by ray tracing method. The differences among the group refractivity models as a function of month and angle of direction were given, which lay the foundation for the practical application and precision evaluation of LiDAR.
Generating colored point cloud by the fusion of CCD images and point cloud data can exert both of their superiorities sufficiently, which has been a major method to obtain spatial information of the buildings for building reconstruction, object detection and other applications. Airborne LiDAR and CCD cameras are usually combined on one platform to carry out colored point cloud based on registration. In addition, there is also a new 3D imaging sensor that can acquire point cloud and CCD images with a stable relationship by the mechanism of common optical system, which could generate colored point cloud faster than the former. In the process of fusion, the colored point cloud is possible to absence some building information such as corners and boundaries. Interpolation is an optimistic method to solve the above issue. However, due to the unclear boundaries between building and ground in the point cloud data, the elevation error of the building area is large after interpolation. Therefore, a correction method for the elevation of colored point cloud in building area is proposed in this paper by combining point cloud contour extraction, image region merging and contour regularization. The new method can accurately obtain the edge of the building by the using of stable relationship, thus reducing the elevation interpolation error of the colored point cloud. The effectiveness of the method is validated based on the flight test data of 3D imaging sensor. The accuracy is improved by 33% after elevation correction.
To verify the performance of Ghost Image via Sparsity Constraints (GISC) LiDAR system and evaluate the distance accuracy, on the basis of considering the parameters of the GISC LiDAR system, the research on design, development and setting method of related target is carried out. The measuring accuracy of distance measurement is verified in the field test. The measurement data of the airborne platform loading load are obtained to evaluate the range accuracy of the GISC LiDAR system.
Aimed at remote sensing product validation, such as leaf area index (LAI), a new sampling strategy based on Taylor expansion method (TEM) and computational geometry model (CGM) is proposed in this paper. Firstly, a correlation index (CI) is calculated based on TEM using high-resolution LAI image to choose the field points of in-situ reference data. Secondly, based on the selected field measurements, the CGM model is established for simulating low-resolution LAI image. Thirdly, the points of in-situ reference data are decided according to the gaps between the simulated LAI and the aggregated LAI from high resolution. If the gap is accepted, the sampling strategy is finally established for field measurement. Otherwise, the field measurements should be re-selected and analyzed until the gap is accepted. Finally, the new sampling strategy is analyzed and compared with traditional sampling strategies, and the results indicate that the sampling strategy proposed in this paper is more stable and efficient.
Satellite laser range system measures the distance between the satellite and the surface of the earth by figuring out the transit time of laser pulse. The beam is refracted when it goes through the atmosphere. The atmosphere refraction effect causes laser propagation delay and path bending, which is one of the key factors to restrict the accuracy of laser ranging. In order to improve the accuracy of atmospheric refraction delay correction, it is necessary to strengthen the study of atmospheric group refractivity models and atmospheric refraction delay correction method. According to the data of Xuzhou upper air meteorological station, which are the monthly values of upper limit layers for 30 years (1981-2010) in China, three atmospheric group refractivity models were analyzed and compared. The atmospheric refraction delays to LiDAR were calculated by ray tracing method. The differences among the group refractivity models as a function of month or direction angle were given, which lay the foundation for the practical application and precision evaluation of LiDAR.
Hyperspectral Light Detection And Ranging (Hyperspectral LiDAR), a recently developed technique, combines the advantages of the LiDAR and hyperspectral imaging and has been attractive for many applications. Supercontinuum laser (SC laser), a rapidly developing technique offers hyperspectral LiDAR a suitable broadband laser source and makes hyperspectral Lidar become an installation from a theory. In this paper, the recent research and progressing of the hyperspectral LiDAR are reviewed. The hyperspectral LiDAR has been researched in theory, prototype system, instrument, and application experiment. However, the pulse energy of the SC laser is low so that the range of the hyperspectral LiDAR is limited. Moreover, considering the characteristics of sensors and A/D converter, in order to obtain the full waveform of the echo, the repetition rate and the pulse width of the SC laser needs to be limited. Recently, improving the detection ability of hyperspectral LiDAR, especially improving the detection range, is a main research area. A higher energy pulse SC laser, a more sensitive sensor, or some algorithms are applied in hyperspectral LiDAR to improve the detection distance from 12 m to 1.5 km. At present, a lot of research has been focused on this novel technology which would be applied in more applications.
In order to solve the problem of insufficient classification types and low classification accuracy using traditional discrete LiDAR, in this paper, the waveform features of Full-waveform LiDAR were analyzed and corrected to be used for land covers classification. Firstly, the waveforms were processed, including waveform preprocessing, waveform decomposition and features extraction. The extracted features were distance, amplitude, waveform width and the backscattering cross-section. In order to decrease the differences of features of the same land cover type and further improve the effectiveness of the features for land covers classification, this paper has made comprehensive correction on the extracted features. The features of waveforms obtained in Zhangye were extracted and corrected. It showed that the variance of corrected features can be reduced by about 20% compared to original features. Then classification ability of corrected features was clearly analyzed using the measured waveform data with different characteristics. To further verify whether the corrected features can improve the classification accuracy, this paper has respectively classified typical land covers based on original features and corrected features. Since the features have independently Gaussian distribution, the Gaussian mixture density model (GMDM) was put forward to be the classification model to classify the targets as road, trees, buildings and farmland in this paper. The classification results of these four land cover types were obtained according to the ground truth information gotten from CCD image data of the targets region. It showed that the classification accuracy can be improved by about 8% when the corrected features were used.
High precision matching of linear array multi-sensor is the key to ensure fast stereo imaging. This paper has presented the general principle of active and passive imaging sensor, designed a high precision matching calibration system of linear array multi-sensor based on large-diameter collimator combined with assisted laser light source, and put forward an optical axis parallelism calibration technology suitable for linear array active and passive imaging sensor. This technology makes use of image acquisition system to obtain spot center, in order to match multi-linear array laser receive and transmit optical axes. At the same time, this paper uses linear visible light sources to extract the optical axis of the laser, then completes the parallelism calibration between lasers receive and transmit optical axes of multi-linear array sensors and active and passive optical axis. The matching relationship between the visible pixel and laser radar detecting element can be obtained when using this technique to calibrate the active and passive imaging sensor. And this relationship is applied to the fast stereo imaging experiment of active and passive imaging sensor and gained good imaging effect.