• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.378-381
• /
• 2008

The regulating and relaxing targets in the Land Use Regulation and Total Maximum Daily Loads are influenced by Land cover information. For the providing more accurate land information, this study attempted to generate an impervious surface map using KOMPSAT-2 image which a Korea manufactured high resolution satellite image. The classification progress of this study carried out by tasseled-cap spectral enhancement through each class extraction technique neither existing classification method. KOMPSAT-2 image of this study is enhanced by Soil Brightness Index(SBI), Green vegetation Index(GVI), None-Such wetness Index(NWI). Then ranges of extracted each index in enhanced image are determined. And then, Confidence Interval of classes was determined through the calculating Non-exceedance Probability. Spectral distributions of each class are changed according to changing of Control coefficient(${\alpha}$) at the calculated Non-exceedance Probability. Previously, Land cover classification map was generated based on established ranges of classes, and then, pervious and impervious surface was reclassified. Finally, impervious ratio of reclassified impervious surface map was calculated with blocks in the study area.

• Journal of Korea Multimedia Society
• /
• v.18 no.3
• /
• pp.298-311
• /
• 2015

Vector data represents a map by its coordinate and Raster data represents a map by its pixel. Since these data types have very large data size, data compression procedure is a compulsory process. This paper compare the results from three different methodologies; GIS (Geographic Information System) vector map data compression using DP(Douglas-Peucker) Simplification algorithm, vector data compression based on Bin classification and the combination between two previous methods. The results shows that the combination between the two methods have the best performance among the three tested methods. The proposed method can achieve 4-9% compression ratio while the other methods show a lower performance.

• Korean Journal of Remote Sensing
• /
• v.33 no.1
• /
• pp.79-88
• /
• 2017

The RapidEye satellite sensor has various spectral wavelength bands, and it can capture large areas with high temporal resolution. Therefore, it affords advantages in generating various types of thematic maps, including land cover maps. In this study, we applied a supervised classification scheme to generate high-resolution land cover maps using RapidEye images. To improve the classification accuracy, object-based classification was performed by adding brightness, yellowness, and greenness bands by Tasseled Cap Transformation (TCT) and Normalized Difference Water Index (NDWI) bands. It was experimentally confirmed that the classification results obtained by adding TCT and NDWI bands as input data showed high classification accuracy compared with the land cover map generated using the original RapidEye images.

• Korean Journal of Remote Sensing
• /
• v.20 no.5
• /
• pp.315-327
• /
• 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 Korean Institute of Navigation and Port Research Conference
• /
• 1998.10a
• /
• pp.59-65
• /
• 1998

A thematic land cover map of Pusan port area was developed using Landsat satellite TM(Thematic Mapper) image. Two types of digital data which are road and sea water layer are extracted from existing paper map were overlayed over the developed land cover map. SPIN-2(KNR-1000) image was utilized to make a facility map of JaSungDae port. SPIN-2 image, which has a cell resolution of 1.56 m showed higer accuracy than TM image, which has a cell resolution of 30 m for facility mapping. Overall, the techniques of digital mapping using satellite image are very useful, effective and efficient.

• Ecology and Resilient Infrastructure
• /
• v.8 no.2
• /
• pp.97-111
• /
• 2021

This study was conducted to develop a habitat type classification system and its map based on the ecological characteristics of species, spatial type, vegetation, topography, and geological conditions preferred by species. To evaluate the relationships between species and their habitats in Korean national parks, we prepared a classification standard table for systematic classification of habitat types. This classification system divides habitats into 6 low-level and 59 mid-level ecological classes based on habitat structure. The mid-level system divided forest ecosystems into 20 subtypes, stream and wetland ecosystems into 8 types, coastal ecosystems into 7 types, arable land into 6 types, development land into 9 types, and 1 type of marine ecosystem. A habitat classification map was drawn utilizing square images, detailed vegetation maps, and forest stand maps, based on the above habitat classification system, and it covered 1,461 plots spanning 21 national parks. The habitat classification system and survey protocol, which consider domestic habitat conditions, should be further developed and applied to habitat assessment, to enhance the utility of this study.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2003.04a
• /
• pp.357-364
• /
• 2003

National Geography Institute(NGI, National mapping agency) has been producing national basemap in automated process since middle of 1980's toward the systematic and efficient management of national land. In 1995, Korean government initiated a full-scale implementation of the National Geographic Information System(NGIS) Development Plan. Under the NGIS Development Plan, NGI began to produce digital maps in the scales of 1:1,000, 1:5,000, 1:25,000. However, digital maps of 1:250,000 or less scale, which are currently used for national land planning, were not included in NGIS Development Plan. Also, the existing laws and specifications related to digital maps of 1:250,000 or less scale are not clearly defined. Therefore this study proposed a feature classification system, which defines features that should be represented in digital map of 1:250,000 or less scale.

• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 1999.10c
• /
• pp.765-770
• /
• 1999

The purpose of this study is to obtain land classification map by using remotely sensed data; Landsat TM and SPOT panchromatic, and to compare their results with statistical data and digitized coverage from topographic paper map. The classification was conducted by maximum likelihood method with training sets. The best result was obtained from the Landsat TM merged by SPOT Panchromatic, that is, similar with statistical data. This is caused by setting more precise training sets with the enhanced spatial resolution by using SPOT Panchromatic. The classified map may be useful as a fundamental data to estimate pollutant load in regional scale of agricultural watershed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.24 no.3
• /
• pp.289-297
• /
• 2006

The Land Cover/Land Use Map have been constructed from 1998, which has hierarchical structure according to land cover/land use system. Level 1 classification Map have done using Landsat satellite image over whole Korean peninsula. Level II classification Map have been digitized using IRS-1C, 1D, KOMPSAT and SPOT5 satellite images resolution-merged with low resolution color images. Level II Land Cover/Land Use Map construction by digitizing method, however, is consuming enormous expense for satellite image acquisition, image process and Land Cover/Land Use Map construction. In this paper, the possibility of constructing Level II Land Cover/Land Use Map using hyperspectral satellite image of EO-1 Hyperion, which is studied a lot recently, is studied. The comparison of classifications using Hyperion satellite image offering more spectral information and Landsat-7 ETM+ image is performed to evaluate the availability of Hyperion satellite image. Also, the algorithm of the optimal band selection is presented for effective application of hyperspectral satellite image.

• The KIPS Transactions:PartD
• /
• v.9D no.5
• /
• pp.939-944
• /
• 2002

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.