• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.119-129
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• 2010

Many algorithms for processing LIDAR data have been developed for diverse applications not limited to patch segmentation, bare-earth filtering and building extraction. However, since we cannot exactly know the true locations of individual LIDAR points, it is difficult to assess the performance of a LIDAR data processing algorithm. In this paper, we thus attempted the performance assessment of the segmentation algorithm developed by Lee (2006) using the LIDAR data generated through simulation based on sensor modelling. Consequently, based on simulation, we can perform the performance assessment of a LIDAR processing algorithm more objectively and quantitatively with an automatic procedure.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.173-179
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• 2015

In this paper, object-based classification of urban areas based on a combination of information from lidar and aerial images is introduced. High resolution images are frequently used in automatic classification, making use of the spectral characteristics of the features under study. However, in urban areas, pixel-based classification can be difficult since building colors differ and the shadows of buildings can obscure building segmentation. Therefore, if the boundaries of buildings can be extracted from lidar, this information could improve the accuracy of urban area classifications. In the data processing stage, lidar data and the aerial image are co-registered into the same coordinate system, and a local maxima filter is used for the building segmentation of lidar data, which are then converted into an image containing only building information. Then, multiresolution segmentation is achieved using a scale parameter, and a color and shape factor; a compactness factor and a layer weight are implemented for the classification using a class hierarchy. Results indicate that lidar can provide useful additional data when combined with high resolution images in the object-oriented hierarchical classification of urban areas.

• Current Optics and Photonics
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• v.2 no.2
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• pp.119-124
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• 2018

The errors in retrieved aerosol backscattering coefficients due to different lidar ratios are analyzed quantitatively in this paper. The actual calculation shows that the inversion error of the aerosol backscattering coefficients using the Fernald backward-integration method increases with increasing inversion distance. The greater the error in the lidar ratio, the faster the error in the aerosol backscattering coefficient increases. For the same error in lidar ratio, the smaller actual aerosol backscattering coefficient will get the larger relative error of the retrieved aerosol backscattering coefficient. The errors in the lidar ratios for dust or the cirrus layer have great impact on the retrievals of backscattering coefficients. The interval between the retrieved height and the reference range is one of the important factors for the derived error in the aerosol backscattering coefficient, which is revealed quantitatively for the first time in this paper. The conclusions of this article can provide a basis for error estimation in retrieved backscattering coefficients of background aerosols, dust and cirrus layer. The errors in the lidar ratio of an aerosol layer influence the retrievals of backscattering coefficients for the aerosol layer below it.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.189-198
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• 2021

In this paper, we introduce the software/hardware system that can reliably calculate the distance from sensor to the model regardless of point cloud density. As the 3d point cloud map is widely adopted for SLAM and computer vision, the accuracy of point cloud map is of great importance. However, the 3D point cloud map obtained from Lidar may reveal different point cloud density depending on the choice of sensor, measurement distance and the object shape. Currently, when measuring map accuracy, high reflective bands are used to generate specific points in point cloud map where distances are measured manually. This manual process is time and labor consuming being highly affected by Lidar sparsity level. To overcome these problems, this paper presents a hardware design that leverage high intensity point from three planar surface. Furthermore, by calculating distance from sensor to the device, we verified that the automated method is much faster than the manual procedure and robust to sparsity by testing with RGB-D camera and Lidar. As will be shown, the system performance is not limited to indoor environment by progressing the experiment using Lidar sensor at outdoor environment.

• Korean Journal of Optics and Photonics
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• v.31 no.2
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• pp.59-70
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• 2020

We discuss a modified numerical model based on the Monte Carlo radiative transfer (MCRT) method, i.e., the MCRT matrix method, for the analysis of atmospheric scattering effects in three-dimensional flash LIDAR systems. Based on the MCRT method, the radiative transfer function for a LIDAR signal is constructed in a form of a matrix, which corresponds to the characteristic response. Exploiting the superposition and convolution of the characteristic response matrices under the paraxial approximation, an extended computer simulation model of an overall flash LIDAR system is developed. The MCRT matrix method substantially reduces the number of tracking signals, which may grow excessively in the case of conventional Monte Carlo methods. Consequently, it can readily yield fast acquisition of the signal response under various scattering conditions and LIDAR-system configurations. Using the computational model based on the MCRT matrix method, we carry out numerical simulations of a three-dimensional flash LIDAR system operating under different atmospheric conditions, varying the scattering coefficient in terms of visible distance. We numerically analyze various phenomena caused by scattering effects in this system, such as degradation of the signal-to-noise ratio, glitches, and spatiotemporal spread and time delay of the LIDAR signals. The MCRT matrix method is expected to be very effective in analyzing a variety of LIDAR systems, including flash LIDAR systems for autonomous driving.

• Journal of the Optical Society of Korea
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• v.14 no.3
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• pp.215-220
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• 2010

Aerosol observation with Raman LIDAR in NIES (National Institute for Environmental Studies, Japan) LIDAR network was conducted from 17 April to 12 June 2008 over Beijing, China. The aerosol optical properties derived from Raman LIDAR were compared with the retrieved data from sun photometer and sky radiometer observations in the Aerosol Robotic Network (AERONET). The comparison provided the complete knowledge of aerosol optical and physical properties in Beijing, especially in pollution and Asian dust events. The averaged aerosol optical depth (AOD) at 675 nm was 0.81 and the Angstrom exponent between 440 nm and 675 nm was 0.99 during experiment. The LIDAR derived AOD at 532 nm in the planetary boundary layer (PBL) was 0.48, which implied that half of the total AOD was contributed by the aerosol in PBL. The corresponding averaged LIDAR ratio and total depolarization ratio (TDR) were 48.5sr and 8.1%. The negative correlation between LIDAR ratio and TDR indicated the LIDAR ratio decreased with aerosol size because of the high TDR associated with nonspherical and large aerosols. The typical volume size distribution of the aerosol clearly demonstrated that the coarse mode radius located near 3 ${\mu}m$ in dust case, a bi-mode with fine particle centered at 0.2 ${\mu}m$ and coarse particle at 2 ${\mu}m$ was the characteristic size distribution in the pollution and clean cases. The different size distributions of aerosol resulted in its different optical properties. The retrieved LIDAR ratio and TDR were 41.1sr and 19.5% for a dust event, 53.8sr and 6.6% for a pollution event as well as 57.3sr and 7.2% for a clean event. In conjunction with the observed surface wind field near the LIDAR site, most of the pollution aerosols were produced locally or transported from the southeast of Beijing, whereas the dust aerosols associated with the clean air mass were transported by the northwesterly or southwesterly winds.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.139-145
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• 2006

The advantage of the lidar data is in fast acquisition and process time as well as in high accuracy and high point density. However lidar data itself is difficult to classify the earth surface because lidar data is in the form of irregularly distributed point clouds. In this study, we investigated land cover classification using both lidar data and optical image through a supervised classification method. Firstly, we generated 1m grid DSM and DEM image and then nDSM was produced by using DSM and DEM. In addition, we had made intensity image using the intensity value of lidar data. As for optical images, the red, blue, green band of CCD image are used. Moreover, a NDVI image using a red band of the CCD image and infrared band of IKONOS image is generated. The experimental results showed that land cover classification with lidar data and optical image together could reach to the accuracy of 74.0%. To improve classification accuracy, we further performed re-classification of shadow area and water body as well as forest and building area. The final classification accuracy was 81.8%.

• Atmosphere
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• v.25 no.1
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• pp.169-177
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• 2015

Vertical distribution of particle mass concentrations was estimated from 8-year elastic-backscatter lidar and sky radiometer data, and from ground-level PM10 concentrations measured in Seoul. Lidar ratio and mass extinction efficiency were determined from aerosol optical depth (AOD) and ground-level PM10 concentrations, which were used as constraints to estimate particle mass concentration. The mean lidar ratio (with standard deviation) and mass extinction efficiency for the entire 8-year study period were $60.44{\pm}23.17$ sr and $3.69{\pm}3.00m^2g^{-1}$, respectively. The lidar ratio did not vary significantly with the ${\AA}ngstr{\ddot{o}}m$ exponent (less than ${\pm}10%$); however, the mass extinction efficiency decreases to $1.82{\pm}1.67m^2g^{-1}$ (51% less than the mean value) when the ${\AA}ngstr{\ddot{o}}m$ exponent is less than 0.5. This result implies that the particle mass concentration from lidar measurements can be underestimated for dust events. Seasonal variation of the particle mass concentration estimated from lidar measurements for the boundary layer, was quite different from ground-level PM10 measurements. This can be attributable to an inhomogeneous vertical distribution of aerosol in the boundary layer.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.20-27
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• 2012

The flowed soil volume is able to be estimated simply from topographic data of before and after the debris flow. However, it is often difficult to obtain high resolution topographic data before debris flow because debris flow was occurred in mountainous area and airborne Lidar data was mainly surveyed in urban area. For this reason, Woo(2011) developed the topographic restoration method that can reconstruct the topography before the debris flow using airborne Lidar data. In this study, we applied the topographic restoration method on Inje county, Bongwha county and Jecheon city, produced topography data before debris flow that RMSE is from 0.16 to 0.34 m. Also, a soil variation was analyzed by topography data before and after debris flow, and it was used to estimate a real soil volume flowed to downstream and a spatial distribution showing collapses, flows, sedimentations appeared to debris flow.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1049-1050
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• 2011