• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.749-762
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• 2020

Precipitation is closely related to various hydrometeorological phenomena, such as runoff and evapotranspiration. In Korean Peninsula, observing rainfall intensity using weather radar and rain gauge network is dominating due to their accurate, intuitive and precise detecting power. However,since these methods are not suitable at ungauged regions, rainfall detection using satellite is required. Satellite-based rainfall data has coarse spatial resolution (10 km, 25 km), and has a limited range of usage due to its reliability of data. The aim of this study is to obtain finer scale precipitation. Especially, to make the applicability of satellite higher at ungauged regions, 10 km satellite-based rainfall data was downscaled to 1 km data using MODerate Resolution Imaging Spectroradiometer (MODIS) based cloud property. Downscaled precipitation was verified in urban region, which has complex topographical and environmental characteristics. Correlation coefficient was similar in summer (+0), decreased in spring (-0.08) and autumn (-0.01), and increased in winter (+0.04) season compared to Global Precipitation Measurement (GPM) based precipitation. Downscaling without calibration using in situ data could be useful in areas where rain gauge system is not sufficient or ground observations are rarely available.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.283-290
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• 2016

Even though existing methods for orthophoto production in traditional photogrammetry are effective in large areas, they are inefficient when dealing with change detection of geometric features and image production for short time periods in small areas. In recent years, the UAV (Unmanned Aerial Vehicle), equipped with various sensors, is rapidly developing and has been implemented in various ways throughout the geospatial information field. The data and imagery of specific areas can be quickly acquired by UAVs at low costs and with frequent updates. Furthermore, the redundancy of geospatial information data can be minimized in the UAV-based orthophoto generation. In this paper, the orthophoto and DEM (Digital Elevation Model) are generated using a standard low-end UAV in small sloped areas which have a rather low accuracy compared to flat areas. The RMSE of the check points is σ_H = ±0.12 m on a horizontal plane and σ_V = ±0.09 m on a vertical plane. As a result, the maximum and mean RMSE are in accordance with the working rule agreement for the airborne laser scanning surveying of the NGII (National Geographic Information Institute) on a 1/500 scale digital map. Through this study, we verify the possibilities of the orthophoto generation in small slope areas using general-purpose low specification UAV rather than a high cost surveying UAV.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.380-389
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• 2007

In Korea, undoubtedly, the cancer is one of the most common reasons of death, and hepatoma is the second highest fatal cancer regardless of the gender only next to the stomach cancer In the middle and prime-aged between 40 and 60 years, the incidence of hepatoma is the highest in the world, and the death rate due to hepatoma is the highest among OECD countries. In this paper, we propose a novel method for automatic identification of hepatoma from a contrast enhanced CT images, which is used in an expert system that helps medical specialists. First, consecutive $40{\sim}50$ contrail enhanced CT images are photographed by every 5mm from the upper part of the chest, and using position information on the rib, we classify the internal area including only internal organs and the external one that consists of the rib, subcutaneous fat layers, and the background from the CT images. Then, the region of the liver is extracted from the classified internal area by using information on the intensity, the distribution of brightness, and using the regions extracted from consecutive images, we restore information on the 5 mm space occurred between the consecutive two slides tty applying a shape-based interpolation method. Lastly, using the characteristics such as the brightness and the morphology, we are able to extract the regions of hepatoma. The expert system based on our method is sufficiently competitive when it is compared with the diagnoses by specialists in the diagnostic radiology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.255-257
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• 2017

These days automotive markets are launching models that include a variety of IT technologies. Tesla's Tesla model S and Google's unmanned automobiles are emerging one after another. This type of automobile with IT technology provides various convenience to the driver and the driver is getting benefit by various conveience services. on the contrary, it is also true that defects for errors in electronic components cause accidents that threaten the safety of drivers. There is a sudden unintended acceleration among these accidents. The cause of the accident is not clear yet, but the claim that the ECU device caused by the magnetic field causes accident of the car due is the most reliable. But, in Korea, when occur a car sudden unintended acceleration accident, the char maker often claims that an accident occurred due to driver's pedal malfunction. Also most drivers are responsible for the lack of grounds to refute. In this paper, the pedal operation image of the driver is acquired and the sensor is attached to the control part such as the excel and brake so as to discriminate whether the vehicle sudden unintended acceleration accident is the driver's pedal operation error or the fault of. i have implemented a system that can do this.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.123-131
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• 2010

In this paper we applied Ground-Based Synthetic Aperture Radar(GB-SAR) interferometry to detect artificial displacement of a reflector and performed an atmospheric humidity correction to improve the accuracy. A series of GB-SAR images were obtained using a center frequency of 5.3 GHz with a range resolution of 25 cm and a azimuth resolution of $0.324^{\circ}$, all in full-polarization (HH, VV, VH, HV) modes. A triangular trihedral corner reflector was located 160 m away from the system, and the artificial displacements of 0-40 mm was implemented during the GB-SAR image acquisition. The result showed that the RMS error between the actual and measured displacements, averaged in all polarization data, was 1.22 mm, while the maximum error in case of the 40 mm displacement was 2.72 mm at HH-polarization. After the atmospheric correction with respect to the humidity, the RMS error was reduced to 0.52 mm. We conclude that a GB-SAR system can be used to monitor the possible displacement of artificial/natural scatterers and the stability assessment with sub-millimeter accuracy.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.184-193
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• 2021

The system for managing spatial data updates the existing information by extracting only the information that is different from the existing information for the newly obtained spatial information file to update the stored information. In order to extract only objects that have changed from existing information, it is necessary to compare whether there is any difference from existing information for all objects included in the newly obtained spatial information file. This study was conducted to improve this total inspection method in a situation where the amount of spatial information that is frequently updated increases and data update is required at the national level. In this study, before inspecting individual objects in a new acquisition space information file, a method of determining whether individual space objects have been changed only by the information in the file was considered. Spatial data files have structured data characteristics different from general image or text document files, so it is possible to determine whether to change the file unit in a simpler way compared to the existing method of creating and managing file hash. By reducing the number of target files that require full inspection, it is expected to improve the use of resources in the system by saving the overall data quality inspection time and saving data extraction time.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.105-118
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• 2022

The demand for information related to 3D spatial objects model in metaverse, smart cities, digital twins, autonomous vehicles, urban air mobility will be increased. 3D model construction for spatial objects is possible with various equipments such as satellite-, aerial-, ground platforms and technologies such as modeling, artificial intelligence, image matching. However, it is not easy to quickly detect and convert spatial objects that need updating. In this study, based on spatial information (features) and attributes, using matching elements such as address code, number of floors, building name, and area, the converged building DB and the detected building DB are constructed. Both to support above and to verify the suitability of object selection that needs to be updated, one system prototype was developed. When constructing the converged building DB, the convergence of spatial information and attributes was impossible or failed in some buildings, and the matching rate was low at about 80%. It is believed that this is due to omitting of attributes about many building objects, especially in the pilot test area. This system prototype will support the establishment of an efficient drone shooting plan for the rapid update of 3D spatial objects, thereby preventing duplication and unnecessary construction of spatial objects, thereby greatly contributing to object improvement and cost reduction.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.21-29
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• 2010

Nakdong Estuary Delta plays various roles of worldwide habitat for migratory birds and a sand supplier to Haewoondae Beach and Gwanganri, which are tourist attractions of Busan. In this study, long-term topographical changes of Nakdong Estuary (Jinwoo Islet, Sinja Islet, Doyodeung, Dadae Beach) coast were detected and interpreted. Through the analysis of 34 years' satellite images, it was found out that a part in between front side and back side of Jinwoo Islet increased, Sinja Islet was divided into two belts in 1970, and has formed an islet since the 1980s and extended westward. Due to the rapid development of small islets in front of Baekhabdeung since 1990s, Doyodeung formed in the late 1990s and is still growing. To make coastal map of Nakdong Estuary area, 13 images, of which the tide level was $99{\pm}13cm$, from the 112 Landsat images of the period from 1975 to 2009 were selected to section into water zone and land zone using NDV. And the rates of coastal line change such as MATLAB EPR(End Point Rate) and LRR(Linear Regression Rate) were calculated using DSAS 4.0(Digital Shoreline Analysis System). Through detecting topographical changes, EPR showed that the front(south) and back side(north) of Jinwoo Islet moved southward at -0.93~2.56m/yr, and changes in costal line and area of Jinwoo Islet were low and stable. The front and backside of Sinja Islet moved northward at 1~4m/yr, whereas the west side of Sinja Islet was stable at 2~3m/yr and east side of Sinja Islet moved northward at 10m/yr or faster. The front and back side of Doyodeung moved northward at 18~27m/yr, causing the increase of area, while the coastal line of Dadae Beach moved westward at 7m/yr, causing the expansion of the beach. LRR also demonstrated a similar trend to EPR. Although analysis of satellite images and GIS could enabled detection of topographical changes and quantitative analysis of natural phenomena, we found that continuous observation of natural phenomena and various analytical methods are required.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.35-45
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• 2018

Image data collected by an airborne hyperspectral camera system have a great usability in coastal line mapping, detection of facilities composed of specific materials, detailed land use analysis, change monitoring and so forh in a complex coastal area because the system provides almost complete spectral and spatial information for each image pixel of tens to hundreds of spectral bands. A few approaches after classifying by a few approaches based on SAM(Spectral Angle Mapper) supervised classification were applied for extracting optimal land cover information from hyperspectral images acquired by CASI-1500 airborne hyperspectral camera on the object of a coastal area which includes both land and sea water areas. We applied three different approaches, that is to say firstly the classification approach of combined land and sea areas, secondly the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas, and thirdly the land area-only classification approach using atmospheric correction images and compared classification results and accuracies. Land cover classification was conducted respectively by selecting not only four band images with the same wavelength range as IKONOS, QuickBird, KOMPSAT and GeoEye satelllite images but also eight band images with the same wavelength range as WorldView-2 from 48 band hyperspectral images and then compared with the classification result conducted with all of 48 band images. As a result, the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas is more effective than classification approach of combined land and sea areas. It is showed the bigger the number of bands, the higher accuracy and reliability in the reclassification approach referred above. The results of higher spectral resolution showed asphalt or concrete roads was able to be classified more accurately.