• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.82-87
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• 2002

RFM is the sensor model of IKONOS imagery for end-users. IKONOS imagery vendors provide RPC (Rational Polynomial Coefficients), Ration Function Model coefficients for IKONOS, for end-users with imagery. So it is possible that end-users obtain geospatial information in their IKONOS imagery without additional any effort. But there are requirements still fur rigorous 3D positions on RPC user. Provided RPC can not satisfy user and company to generate precision 3D terrain model. In IKONOS imagery, physical sensor modeling is difficult because IKONOS vendors do not provide satellite ephemeris data and abstract sensor modeling requires many GCP well distributed in the whole image as well as other satellite imagery. Therefore RPC modification is better choice. If a few GCP are available, RPC can be modified by method which is introduced in this paper. Study on evaluation modified RPC in IKONOS reports reasonable result. Pseudo GCP generated with vendor's RPC and additional GCP make it possible through sequential solution.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1131-1133
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• 2003

Spatial image processing software requires various user interactions to make a plan, prepare necessary data such as images, vectors, ancillary data and user-defined data, execute functions according to pre-defined procedures, analyze and store the results. In this manner, overall processes are controlled by user interactions. In this paper, we propose visual modeler which has the automated spatial image processing technique to minimize user interactions and re -use repeatable procedure. The proposed visual modeler is designed to use inter-operable components proposed by OpenGIS consortium as well as conventional COM components.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.274-277
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• 2007

This study is to estimate the spatial distribution of soil loss using the land use data produced from QuickBird satellite imagery. For a small agricultural watershed (1.16 $km^2$) located in the upstream of Gyeongan-cheon watershed, a precise agricultural land use map were prepared using QuickBird satellite image of April 5 of 2003. RUSLE (Revised Universal Soil Loss Equation) was adopted for soil loss estimation. The data (DEM, soil and land use) for the RUSLE were prepared for 5 m and 30 m spatial resolution. The results were compared with each other and the result of 30 m Landsat land use data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1140-1142
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• 2003

Imagery and gridded data can be used as the sources of extracting important information and data layers in utilizing GIS. The existing metadata standard to distribute and to utilize the geographic information are mainly concentrated at the vector data and do not provide metadata components for imagery and gridded data. In this study, metadata components for imagery and gridded data have been investigated. Firstly, existing international metadata standards such as ISO and domestic standards of TTA have been analyzed. Based on th results, the draft metadata for imagery and gridded data have been proposed as the extensions of domestic metadata standard distribution. The draft metadata could contribute to build the basic standards to access and utilize proper imagery and gridded data fit to various application field, and this will be fundamental bases for activating GIS in public and private sectors.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.97-102
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• 2015

In this paper, an extension of the standard common spatial pattern (CSP) algorithm using the strong uncorrelated transform (SUT) is used in order to extract the features for an accurate classification of the left- and right-hand motor imagery tasks. The algorithm is designed to analyze the complex data, which can preserve the additional information of the relationship between the two electroencephalogram (EEG) data from distant channels. This is based on the fact that distant regions of the brain are spatially distributed spatially and related, as in a network. The real-world left- and right-hand motor imagery EEG data was acquired through the Physionet database and the support vector machine (SVM) was used as a classifier to test the proposed method. The results showed that extracting the features of the pair-wise channel data using the strong uncorrelated transform complex common spatial pattern (SUTCCSP) provides a higher classification rate compared to the standard CSP algorithm.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.577-587
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• 2023

The utilization of crowdsourced spatial data has been actively researched; however, issues stemming from the uncertainty of data quality have been raised. In particular, when low-quality data is mixed into drone imagery datasets, it can degrade the quality of spatial information output. In order to address these problems, the study presents a methodology for automatically validating the geometric quality of crowdsourced imagery. Key quality factors such as spatial resolution, resolution variation, matching point reprojection error, and bundle adjustment results are utilized. To classify imagery suitable for spatial information generation, training and validation datasets are constructed, and machine learning is conducted using a radial basis function (RBF)-based support vector machine (SVM) model. The trained SVM model achieved a classification accuracy of 99.1%. To evaluate the effectiveness of the quality validation model, imagery sets before and after applying the model to drone imagery not used in training and validation are compared by generating orthoimages. The results confirm that the application of the quality validation model reduces various distortions that can be included in orthoimages and enhances object identifiability. The proposed quality validation methodology is expected to increase the utility of crowdsourced data in spatial information generation by automatically selecting high-quality data from the multitude of crowdsourced data with varying qualities.

• Korean Journal of Remote Sensing
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• v.9 no.1
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• pp.1-19
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• 1993

A simple bispectral threshold technique which reflects the temporal and spatial characteristics of the analysis area has been developed to classify the cloud type and estimate the cloud cover from GMS/S-VISSR(Stretched Visible and Infrared Spin Scan Radiometer) imagery. In this research, we divided the analysis area into land and sea to consider their different optical properties and used the same time observation data to exclude the solar zenith angle effects included in the raw data. Statistical clear sky radiance(CSRs) was constructed using maximum brightness temperature and minimum albedo from the S-VISSR imagery data during consecutive two weeks. The CSR used in the cloud anaysis was updated on the daily basis by using CSRs, the standard deviation of CSRs and present raw data to reflect the daily variation of temperature. Thresholds were applied to classify the cloud type and estimate the cloud cover from GMS/S-VISST imagery. We used a different thresholds according to the earth surface type and the thresholds were enough to resolve the spatial variation of brightness temperature and the noise in raw data. To classify the ambiguous pixels, we used the time series of 2-D histogram and local standard deviation, and the results showed a little improvements. Visual comparisons among the present research results, KMA's manual analysis and observed sea level charts showed a good agreement in quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.499-505
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• 2007

A method of improving the correctness and confidence in land use classification as well as urban spatial structure analysis of local governments using GIS and satellite imagery is suggested. This study also compares and analyzes LSAS (Land Suitability Assessment System) results using two approaches-LSAS with priority classification, and LSAS using standard estimation factors without priority classification. The conclusions that can be drawn from this study are as follows. First, a method of maintaining up-to-date local government data by updating the LSAS database using high-resolution satellite imagery is suggested. Second, to formulate a scientific and reasonable land use plan from the viewpoint of territory development and urban management, a method of simultaneously processing the two described approaches is suggested. Finally, LSAS was constructed by using varieties of land information such as the cadastral map, the digital topographic map, varieties of thematic maps, and official land price data, and expects to utilize urban management plan establishment widely and effectively through regular data updating and problem resolution of data accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.2 s.36
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• pp.55-62
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• 2006

Droughts have been an important factor in disaster management in Korea because she has been grouped into nations of lack of water. Satellite imagery can be applied to droughts monitoring because it can provide periodic data for large area for long time. This study aims to present a process to analyze droughts in large area using satellite imagery. We estimated evapotranspiration in large area using NDVI data acquired from satellite imagery. For satellite imagery, we dealt with MODIS data operated by NASA. The evapotranspiration estimated from satellite imagery was combined with precipitation data and potential evapotranspiration data to estimate water balances. Using water balances we could analyze droughts effectively in our object area. As the result of this study, we could increase the usability of satellite imagery, especially in droughts analysis.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.135-143
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• 2012

Remote sensing which observes repeatedly the whole Earth and GIS-based decision-making technology have been utilized widely in disaster management such as early warning monitoring, damage investigation, emergent rescue and response, rapid recovery etc. In addition, various countermeasures of national level to collect timely satellite imagery in emergency have been considered through the operation of a satellite with onboard multiple sensors as well as the practical joint use of satellite imagery by collaboration with space agencies of the world. In order to respond heavy snowfall disaster occurred on the east coast of the Korean Peninsula in February 2011, snow-covered regions were analyzed and detected in this study through NDSI(Normalized Difference Snow Index) considering reflectance of wavelength for MODIS sensor and change detection algorithm using satellite imagery collected from International Charter. We present the application case of National Disaster Management Institute(NDMI) which supported timely decision-making through GIS spatial analysis with various spatial data and snow cover map.