• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1709-1718
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• 2021

The waveform data of the Airborne Bathymetric LiDAR (ABL; LiDAR: Light Detection And Ranging) system provides data with improved accuracy, resolution, and reliability compared to the discrete-return data, and increases the user's control over data processing. Furthermore, we are able to extract additional information about the return signal. Waveform decomposition is a technique that separates each echo from the received waveform with a mixture of water surface and seabed reflections, waterbody backscattering, and various noises. In this study, a new waveform decomposition technique based on a Gaussian model was developed to improve the point extraction performance from the ABL waveform data. In the existing waveform decomposition techniques, the number of decomposed echoes and decomposition performance depend on the peak detection results because they use waveform peaks as initial values. However, in the study, we improved the approximation accuracy of the decomposition model by adding the estimated potential peak candidates to the initial peaks. As a result of an experiment using waveform data obtained from the East Coast from the Seahawk system, the precision of the decomposition model was improved by about 37% based on evaluating RMSE compared to the Gaussian decomposition method.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.941-951
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• 2018

Utilization and demand of UAV (unmanned aerial vehicle) for the generation of 3D city model are increasing. In this study, we performed an experiment to adjustment position/orientation of UAV with incomplete attitude information and to extract point cloud data. In order to correct the attitude of the UAV, the rotation angle was calculated by using the continuous position information of UAV movements. Based on this, the corrected position/orientation information was obtained by applying IBA (Incremental Bundle Adjustment) based on photogrammetry. Each pair was transformed into an epipolar image, and the MDR (Multi-Dimensional Relaxation) technique was applied to obtain high precision DSM. Each extracted pair is aggregated and output in the form of a single point cloud or DSM. Using the DJI inspire1 and Phantom4 images, we can confirm that the point cloud can be extracted which expresses the railing of the building clearly. In the future, research will be conducted on improving the matching performance and establishing sensor models of oblique images. After that, we will continue the image processing technology for the generation of the 3D city model through the study of the extraction of 3D cloud It should be developed.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.893-903
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• 2018

The necessity of automatic precise georeferencing is increasing with the increase applications of high-resolution satellite imagery. One of the methods for collecting ground control points (GCPs) for precise georeferencing is to use chip images obtained by extracting a subset of an image map such as an ortho-aerial image, and can be automated using an image matching technique. In this case, the importance of the image matching success rate is increased due to the limitation of the number of the chip images for the known reference points such as the unified control point. This study aims to propose a method to improve the success rate of image matching between KOMPSAT-3A images and GCP chip images from aerial ortho-images. We performed the image matching with 7 cases of band pair using KOMPSAT-3A panchromatic (PAN), multispectral (MS), pansharpened (PS) imagery and GCP chip images, then compared matching success rates. As a result, about 10-30% of success rate is increased to about 40-50% when using PS imagery by using PAN and MS imagery. Therefore, using PS imagery for image matching of KOMPSAT-3A images and aerial ortho-images would be helpful to improve the matching success rate.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.577-589
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• 2021

Drone mapping systems can be applied to many fields such as disaster damage investigation, environmental monitoring, and construction process monitoring. To integrate individual sensors attached to a drone, it was essential to undergo complicated procedures including time synchronization. Recently, a variety of composite sensors are released which consist of visual sensors and GPS/INS. Composite sensors integrate multi-sensory data internally, and they provide geotagged image files to users. Therefore, to use composite sensors in drone mapping systems, mapping accuracies from composite sensors should be examined. In this study, we analyzed the mapping accuracies of a composite sensor, focusing on the data acquisition area and pre-calibration effect. In the first experiment, we analyzed how mapping accuracy varies with the number of ground control points. When 2 GCPs were used for mapping, the total RMSE has been reduced by 40 cm from more than 1 m to about 60 cm. In the second experiment, we assessed mapping accuracies based on whether pre-calibration is conducted or not. Using a few ground control points showed the pre-calibration does not affect mapping accuracies. The formation of weak geometry of the image sequences has resulted that pre-calibration can be essential to decrease possible mapping errors. In the absence of ground control points, pre-calibration also can improve mapping errors. Based on this study, we expect future drone mapping systems using composite sensors will contribute to streamlining a survey and calibration process depending on the data acquisition circumstances.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1837-1846
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• 2022

The annual increase in chemical accidents is causing damage to life and the environment due to the spread and residual of substances. Environmental damage investigation is more difficult to determine the geographical scope and timing than human damage investigation. Considering the reality that there is a lack of professional investigation personnel, it is urgent to develop an efficient quantitative evaluation method. In order to improve this situation, this paper conducted a chemical accidents investigation using unmanned aerial vehicles(UAV) equipped with various sensors. The damaged area was calculated by Ortho-image and strength of agreement was calculated using the normalized difference vegetation index image. As a result, the Cohen's Kappa coefficient was 0.649 (threshold 0.7). However, there is a limitation in that analysis has been performed based on the pixel of the normalized difference vegetation index. Therefore, there is a need for a chemical accident investigation plan that overcomes the limitations.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.827-835
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• 2023

Drone light detection and ranging (LiDAR) is a state-of-the-art surveying technology that enables close investigation of the top of the mountain slope or the inaccessible slope, and is being used for field surveys in mountainous terrain. To build topographic information using Drone LiDAR, a preprocessing process is required to effectively separate ground and non-ground points from the acquired point cloud. Therefore, in this study, the point group data of the mountain topography was acquired using an aerial LiDAR mounted on a commercial drone, and the application and accuracy of the cloth simulation filtering algorithm, one of the ground separation techniques, was verified. As a result of applying the algorithm, the separation accuracy of the ground and the non-ground was 84.3%, and the kappa coefficient was 0.71, and drone LiDAR data could be effectively used for landslide field surveys in mountainous terrain.

• Spatial Information Research
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• v.16 no.3
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• pp.373-383
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• 2008

LiDAR, unlike satellite imagery and aerial photographs, which provides irregularly distributed three-dimensional coordinates of ground surface, enables three-dimensional modeling. In this study, urban area was classified based on 3D information collected by LiDAR. Morphological and spatial properties are determined by the ratio of ground and non-ground point that are estimated with the number of ground reflected point data of LiDAR raw data. With this information, the residential and forest area could be classified in terms of height and density of trees. The intensity of the signal is distinguished by a statistical method, Jenk's Natural Break. Vegetative area (high or low density) and non-vegetative area (high or low density) are classified with reflective ratio of ground surface.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.47-52
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• 2008

The visual implementation of 3-dimensional national environment is focused by the requirement and importance in the fields such as, urban planing, telecommunication facility deployment plan, railway construction, construction engineering, spatial city development, safety and disaster prevention engineering. The currently used DEM system based on the 2-D digital maps and contour lines has limitation in implementation in reproducing the 3-D spatial city. Currently, the LiDAR data which combines the laser and GPS skill has been introduced to obtain high resolution accuracy in the altitude measurement in the advanced country. In this paper, we first introduce the LiDAR based researches in advanced foreign countries, then we propose the data generation scheme and an solution algorithm for the optimal management of our 3-D spatial u-City construction. For this purpose, LiDAR based height data transformed to DEM, and the realtime unification of the vector via digital image mapping and raster via exactness evaluation is transformed to make it possible to trace the model of generated 3-dimensional model with long distance for 3D u-city model generation.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.140-148
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• 2001

The satellite sensor model is typically established using ground control points acquired by ground survey Of existing topographic maps. In some cases where the targeted area can't be accessed and the topographic maps are not available, it is difficult to obtain ground control points so that geospatial information could not be obtained from satellite image. The paper presents several satellite sensor models and satellite image decomposition methods for non-accessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then the behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in 1$^{st}$, 2$^{nd}$ and 3$^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\phi$(phi) correlated highly with positional parameters could be assigned to constant values. For non-accessible area, satellite images were decomposed, which means that two consecutive images were combined as one image. The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1$^{st}$ order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.