Dr. Keunyong Kim Profile

Dr. Keunyong Kim

at Korea Institute of Ocean Science & Technology

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Publications (2)

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Proceedings Article | 19 October 2023 Poster + Paper

A study on the automatic extraction of trajectory for LIDAR data correction

YeongJae Jang, Keunyong Kim, Joo-Hyung Ryu

Proceedings Volume 12731, 127310R (2023) https://doi.org/10.1117/12.2679559

KEYWORDS: LIDAR, Point clouds, Unmanned aerial vehicles, Data corrections, Data acquisition, Reflection, Data modeling, Calibration, Sensors, Reflectivity

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Generally, LiDAR sensors use near-infrared light, so in highly water contented areas such as marshes, the laser reflection intensity is weakened due to the absorption of a lot of light. In addition, during the IMU calibration process of the sensor, it is not possible to obtain a high-precision point cloud due to the shaking of the aircraft, accumulation of errors, and other factors. To post-process the acquired point cloud, it is necessary to separate the data that contains the vibrations of the aircraft, such as acceleration, rotation, and calibration, from the point cloud by aligning the trajectory of the UAV with the point cloud. However, manually separating the trajectory for a wide area of UAV flight can take a lot of time and can affect the consistency of the data. In this study, aim to extract a stable LiDAR point cloud by separating the trajectory of the UAV based on the following criteria for UAVs with a certain pattern of trajectory. First, separate the two trajectories by distinguishing between acceleration and cruising of the UAV. Second, separate two regions where the direction of the UAV's travel changes sharply. Finally, apply a process to separate the IMU calibration process. Through this process, can automatically extract the LiDAR trajectory data and select only the point cloud obtained at the same flight speed and altitude, thereby obtaining a point cloud density of a constant value. This study reduces the time required for separating and post-processing the trajectory of LiDAR data and enables the production of high-resolution terrain data for a wide area that needs to be flown at low altitudes.

Proceedings Article | 15 November 2018 Paper

Satellite-based seagrass mapping in Korean coastal waters

Jong-Kuk Choi, Keunyong Kim, Genki Terauchi

Proceedings Volume 10778, 107780J (2018) https://doi.org/10.1117/12.2326787

KEYWORDS: Image classification, Earth observing sensors, Landsat, Satellites, Spatial resolution, Satellite imaging, Image segmentation, Mahalanobis distance, Atmospheric corrections, Remote sensing

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Seagrass beds provide habitat for invertebrate and fish species, many of which are economically important. In addition, they perform important physical functions such as trapping sediment particulates associated with dissipating wave energy, thus are helpful to maintain clear waters. We, here, generated the map of seagrass distribution using remotely sensed images to which atmospheric corrections and water column corrections had been applied. Then, the seagrass habitat distribution changes were calculated by seagrass habitat map. For this study, we selected Deukryang Bay located on the southern coast of the Korean peninsula. It is surrounded by small villages like Jinmok-ri and Ongam-ri. Zostera marina dominated at the bay, small amounts of Z. caulescens and Halophila nipponica are also distributed in this area. The results showed that image classifications to which the water column correction had been applied produced improved accuracies in all the classification algorithms we had employed. The object-based classification algorithm showed the highest accuracy, but it is effective method for the high spatial resolution remotely sensed images, consequently not suitable for monitoring changes of the long-term base. Thus, we applied the Mahalanobis distance method which had been known to suitable for medium spatial resolution images like Landsat. This study revealed that seagrass beds in the study area showed similar pattern of distribution during recent 20 years.

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