• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.383-387
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• 2007

The improved accuracy of the Land Cover/Land Use Map constructed using Hyperspectal Satellite Image and the possibility of real time classification of Land Use using optimal Band Selective Factor enable the change detection from automatic classification using the existed Land Cover/Land Use Map and the newly acquired Hyperspectral Satellite Image. In this study, the effective analysis techniques for automatic generation of training regions, automatic classification and automatic change detection are proposed to minimize the expert's interpretation for automatic update of the Land Cover/Land Use Map. The proposed algorithms performed successfully the automatic Land Cover/Land Use Map construction, automatic change detection and automatic update on the image which contained the changed region. It would increase applicability in actual services. Also, it would be expected to present the effective methods of constructing national land monitoring system.

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.315-327
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• 2004

Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.426-428
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• 2003

Optical and microwave remote sensing data have been widely used in land cover and land use classification. Thanks to the spectral absorption characteristics of ground object in visible and near infrared region, optical data enables to extract different land cover types according to their material composition like water body, vegetation cover or bare land. On the other hand, microwave sensor receives backscatter radiance which contains information on surface roughness, object density and their 3-D structure that are very important complementary information to interpret land use and land cover. Separate use of these data have brought many successful results in practice. However, the accuracy of the land use / land cover established by this methodology still has some problems. One of the way to improve accuracy of the land use / land cover classification is just combination of both optical and microwave data in analysis. In this paper for the research, the author used LANDSAT TM scene 127/45 acquired on October 21, 1992, JERS-1 SAR scene 119/265 acquired on October 27, 1992 and aerial photographs taken on October 21, 1992. The study area has been selected in Hanoi City and surrounding area, Vietnam. This is a flat agricultural area with various land use types as water rice, secondary crops like maize, cassava, vegetables cultivation as cucumber, tomato etc. mixed with human settlement and some manufacture facilities as brick and ceramic factories. The use of only optical or microwave data could result in misclassification among some land use features as settlement and vegetables cultivation using frame stages. By combination of multitemporal JERS-1 SAR and TM data these errors have been eliminated so that accuracy of the final land use / land cover map has been improved. The paper describes a methodology for data combination and presents results achieved by the proposed approach.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.984-987
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• 2003

In order to rapidly catch up urban region’s detailed land-use or land-cover information; this research used the post-classification algorithm (Spatial Reclassification Kernel: SPARK) to create a land-use map of Taichung City. We discussed the urban land-use classification model with the IKONOS images. The conclusions may be distinguished as follows:(a) Using the Maximum-Likelihood algorithm to classify seven broad land-cover categories. The overall accuracy in this stage achieves 92.72% and Kappa coefficient will be obtained 0.91; and (b) Using the SPARK method to classify images for detect the land-use, the overall accuracy achieves higher 89.64% and Kappa coefficient will be 0.86. To conclude, the research process in this study can fully and carefully describe local land-use pattern and assist the demand of land management and resources planning reference.

• Journal of Korean Society of Rural Planning
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• v.3 no.2
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• pp.102-111
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• 1997

For rational agricultural land use planning, it is quite necessary to get hold of land suitability precisely and to make decision on land use patterns accordingly. In the methodological viewpoint, objective and scientific evaluation techniques for land suitability classification should be supported for the systematic land use planning. As one of technical development approaches to rational land use planning, this study tried to frame a land suitability evaluation system for agricultural purposes. Evaluation unit is defined as a tract of land bounded by road, other land units and topographical features. And quantification theory was applied in the determination works of evaluation criteria. The administrative area of Namsa-myon(district), Yongin-si(city), Kyunggi-do(province) was selected for the case study. In order to check the feasibility of the evaluation system developed in the study, field check team, consisting of 2 government officers and 2 representative farmers, carried out evaluation works by observation on 148 sample land units, 10% of total 1,480 ones. Between estimated and observed results, there showed very good relationship of its multiple correlation coefficient, R=0.9467.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.397-408
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• 2003

The information about land use presents future development and vision being the basis of nation development; therefore, it is necessary to more active research that can detect wide land use and changes for the information and efficient management about land use. In this study, we wished to analyze effectively land use changes to Ansan city that is fast changing land use by the latest national land development and urbanization. this study executed land-cover classification using 4 year's Landsat TM images including Ansan city, and efficiently could manage the result of land-cover changes through Arc/Info GRID analysis. Especially, by using change detection system that is developed in this research, we could variously detect land-cover changes, and query and search easily past land-cover changes of pixels that correspond to specific region.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1221-1223
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• 2003

With speediness of economy, the structure of land use has taken lots of change. How can we quickly and exactly obtain detailed land use/cover change information, and then we know land resource amount, quality, distributing and change direction. More and more high resolution satellite systems are under development. So we can make good use of RS data, existed GIS data and GPS data to extract change information and update map. In this paper a fully automated approach for detecting land use/cover change using remote sensing data with object-oriented classification based on GIS data, GPS data is presented (referring to Fig.1). At same time, I realize integrating raster with vector methods of updating the basic land use/land cover map based on 3S technology and this is becoming one of the most important developing direction in 3S application fields; land-use and cover change fields over the world. It has been successful applied in two tasks of The Ministry of Land and Resources P.R.C and taken some of benefit.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.107-109
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• 2003

Optical sensor-based land cover categories produce spectral signature confusion along with degraded classification accuracy. In the classification tasks, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. This paper describes the result of land cover/land use classification derived from solely of Landsat TM (TM) and multisensor image fusion between JERS 1 SAR (JERS) and TM data. The best radar data manipulation is fused with TM through various techniques. Classification results are relatively good. The highest Kappa Coefficient is derived from classification using principal component analysis-high pass filtering (PCA+HPF) technique with the Overall Accuracy significantly high.

• Korean Journal of Remote Sensing
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• v.2 no.2
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• pp.69-77
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• 1986

This study was emphasized on the land use classification by Remote Sensing Technique. Land cover maps about the major urbans, Seoul, Tajeon regions, its of each classified classes were extracted by use of Landsat MSS Data and Digital Image Processing System. From the results of this study, it was proved that land use classification by Remote Sensing technique could be used to obtain fully fruitful Results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.4
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• pp.385-393
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• 2001

Urban land use information is the base data for the urban planning, district-level planning, traffic impact assessment and environmental impact assessment, etc. The level of detail of the current land use information is not enough to analysis and planning. In this study, the status and problems of the current land use information is analysed. The advanced abroad cases, such as LBCS(Land Based Classification System) of American Planning Association, are studied. The purpose of this study is to develop the coding system for urban land use information classification. Through this system, it is anticipated to standardization of land use classification system and improvement of data compactability.