• Journal of Astronomy and Space Sciences
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• v.20 no.1
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• pp.1-10
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• 2003

A precise kinematic orbit determination (P-KOD) procedure for Low Earth Orbiter(LEO) using the GPS ion-free triple differenced carrier phases is presented. Because the triple differenced observables provide only relative information, the first epoch's positions of the orbit should be held fixed. Then, both forward and backward filtering was executed to mitigate the effect of biases of the first epoch's position. p-KOD utilizes the precise GPS orbits and ground stations data from International GPS Service (IGS) so that the only unknown parameters to be solved are positions of the satellite at each epoch. Currently, the 3-D accuracy off-KOD applied to CHAMP (CHAllenging Min-isatellite Payload) shows better than 35 cm compared to the published rapid scientific orbit (RSO) solution from GFZ (GeoForschungsZentrum Potsdam). The data screening for cycle slips is a particularly challenging procedure for LEO, which moves very fast in the middle of the ionospheric layer. It was found that data screening using SNR (signal to noise ratio) generates best results based on the residual analysis using RSO. It is expected that much better accuracy are achievable with refined prescreening procedure and optimized geometry of the satellites and ground stations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.643-651
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• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).

• 한국지구물리탐사학회:학술대회논문집
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• 2007.12a
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• pp.15-25
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• 2007

Gravity method could be one of the most effective tool for evaluating the soundness of basement which is directly correlated with density and its variations. Moreover, Gravimeter is easy to handle and strong to electromagnetic noises. But, gravity anomaly due to the target structures in engineering and environmemtal applications are too small to detect, comparing to the external changes, such as, elevation, topography, and regional geological variations. Gravity method targeting these kinds of small anomaly sources with high precision usually called microgravity. Microgravimetry with precision and accuracy of few ${\mu}Gal$, can be achieved by the recent high-resolution gravimeter, careful field acquisition, and sophisticated processing, analysis, and interpretation routines. This paper describes the application of the microgravity, such as, density structure of a rock fill dam, detection of abandoned mine-shaft, detection and mapping of karstic cavities in limestone terrains, and time-lapse gravity for grout monitoring. The case studies show how the gravity anomalies detect the location of the targets and reveal the geologic structure by mapping density distributions and their variations.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.113-119
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• 2003

Building information is extremely important for many applications within the urban environment. Sufficient techniques and user-friendly tools for information extraction from remotely sensed imagery are urgently needed. This paper presents an automatic and manual approach for extracting footprints of buildings in urban areas from airborne Light Detection and Ranging (LIDAR) data. First a digital surface model (DSM) was generated from the LIDAR point data. Then, objects higher than the ground surface are extracted using the generated DSM. Based on general knowledge on the study area and field visits, buildings were separated from other objects. The automatic technique for extracting the building footprints was based on different window sizes and different values of image add backs, while the manual technique was based on image segmentation. A comparison was then made to see how precise the two techniques are in detecting and extracting building footprints. Finally, the results were compared with manually digitized building reference data to conduct an accuracy assessment and the result shows that LIDAR data provide a better shape characterization of each buildings.

• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.141-151
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• 2006

A blank test was done to calculatee the car itself's magnetic effect as noise and to eliminate it from the data set of total magnetic intensity(=magnetic flux density) exploration in a car-borne magnetic exploration system. To calculate the induced magnetic intensity(= magnetization) and the remanent magnetic intensity(= magnetization) of the car itself, we have installed the magnetometer on a fixed point and measured the magnetic intensity letting the car move around the magnetometer, and we have changed the data set into an analogous data set as if acquired in the condition that we have parked the car on the same fixed point and measured the magnetic intensity moving the magnetometer around the magnetometer. Through an inversion with the later data set as input, we have calculated the magnetic center and the magnetic moments of the induced magnetic intensity(= magnetization) and the remanent magnetic intensity(= magnetization) of the car itself with the two centers coincided because of some barriers of the inversion algorithm that we have used in this study. On the other hand, we have extracted the magnetic anomaly by reducing i. e. vectorially eliminating the induced magnetic intensity(= magnetization) and the remanent magnetic intensity(= magnetization) of the car itself calculated forwardly, from the magnetic exploration data set acquired by the car-borne magnetic exploration system.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.10a
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• pp.285-287
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• 2006

• Economic and Environmental Geology
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• v.41 no.5
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• pp.583-586
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• 2008

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.313-318
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• 2017

Land surface temperature is essential for monitoring abnormal climate phenomena such as UHI (Urban Heat Islands), and for modeling weather patterns. However, the quality of surface temperature obtained from the optical space imagery is affected by many factors such as, revisit period of the satellite, instance of capture, spatial resolution, and cloud coverage. Landsat 8 imagery, often used to obtain surface temperatures, has a high resolution of 30 meters (100 meters rearranged to 30 meters) and a revisit frequency of 16 days. On the contrary, MODIS imagery can be acquired daily with a spatial resolution of about 1 kilometer. Many past attempts have been made using both Landsat and MODIS imagery to complement each other to produce an imagery of improved temporal and spatial resolution. This paper applied machine learning methods and performed downscaling which can obtain daily based land surface temperature imagery of 30 meters.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.591-600
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• 2018

Recent SAR systems provide fully polarimetric SAR data, which is known to be useful in a variety of applications such as disaster monitoring, target recognition, and land cover classification. The objective of this study is to evaluate the performance of polarization SAR data for landslide detection. The detectability of different SAR parameters was investigated based on the supervised classification approach. The classifier used in this study is the Adaptive Boosting algorithms. A fully polarimetric L-band PALSAR-2 data was used to examine landslides caused by the 2016 Kumamoto earthquake in Kyushu, Japan. Experimental results show that fully polarimetric features from the target decomposition technique can provide improved detectability of landslide site with significant reduction of false alarms as compared with the single polarimetric observables.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1052-1054
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• 2003

RAZAKSAT is a second micro-satellite mission by Malaysian Satellite Program and is expected for launch in June 2004. Designed to orbit the earth at low-equatorial orbit, RAZAKSAT will meet Malaysia’s immediate needs to rapid data acquisition (real time and more repetitions) to address many operational issues of remote sensing applications, which require availability of current data sets. RAZAKSAT will be among the first remote sensing satellite to orbit the earth at low inclination along the equator, 9$^{\circ}$ with 685km altitude, hence, allows optimal geographical information and environment change within equatorial region be observed with a unique revisit characteristics. The satellite primary payload is MAC, a push-broom type camera with 2.5m of ground sampling distance (GSD) in panchromatic band and 5m of GSD in four multi-spectral bands. This paper describes on the variation of illumination anticipated from simulated RAZAKSAT image, examine its implication to its ground leaving radiances for major applications.