• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.16 no.2
• /
• pp.187-194
• /
• 1998

In this study, effective multispectral Landsat TM band combinations for a merging with the high resolution IRS-1C PAN data using the IHS method to improve landuse accuracy is discussed. From the pre-classified image using the merged images with TM all six band images(with the exception of band 6 image) and PAN image, a sample data which has ten classes was generated. An evaluation of the overall classification accuracy for the representative seven merged images which were merged using each TM three-band images and IRS-1C PAN image by IHS method for the sample area. The increase in classification accuracy is most significant with the inclusion of two of TM4, TM5 and TM7 infrared band images. Especially, the largest increase(11.8 percent) in landuse classification accuracy were investigated when Landsat TM247 bands were merged with IRS-1C PAN data. The classification accuracy when TM three band image and PAN image were used without merging is higher than result of the case of using the merged images.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.211-214
• /
• 2007

After the forest fire, as access is not easy, forest damage degree are determined with Landsat TM image rather than visual inspection. Therefore in this study, damaged areas are extracted with factor analysis and cluster analysis. Second factor analysis was performed for areas suspicious as forest fire damage areas to evaluate accuracy after separating into strong, medium and light forest fire areas.

• Korean Journal of Remote Sensing
• /
• v.7 no.1
• /
• pp.29-43
• /
• 1991

The study was performed to extraction the ground water potential area using LANDSAT TM data. The image processing techniques developed for the study are contrast transformation, differential filtering and pseudo stereoscopic image methods. These were examined for lineament extraction, lineament interpretation and the integration of vertor data with LANDSAT data. The differential filtering method is much usefull for lineament extraction, and all direction lineaments are clearly shown on the band 5 image of LANDSAT TM. The pseudo stereoscopic image are made in which color differential method is adopted, the pair images are usefull for the lineament interpretation. The results of the analysis are as follows. 1) there is a close correlation between lineament and cased well in the study area, because 33 wells of the developed 45 cased wells coincide with the lineaments. 2) 21 sites in the study area were selected for pumping test, and as a result 11 sites of them produces over than 200 ton/day.

• Proceedings of the Korean Environmental Sciences Society Conference
• /
• 2003.11a
• /
• pp.33-36
• /
• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.

• Journal of Environmental Impact Assessment
• /
• v.4 no.2
• /
• pp.59-70
• /
• 1995

The solution-state of a reclaimed waste would be known to the method in using an analysis for seepage. But it is not the best method in the huge landfill reclaimed all kinds of the waste at random. Especially in case of the landfill called the Nan Gi-do located along the Han-river, it is difficult to judge the generative seepage to be flowed in to the Han-river. So to plan the effective stabilization on a landfill, it is very useful survey method using the Landsat TM image. Operating a heat-distribution analysis with the Landsat TM image, in case of a landfill not having definite data, we would assume the reclaimed sections of the waste to judge a solution-speed late comparatively such as a industry waste or a harmful waste through the heat change.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.696-701
• /
• 2002

This paper describes some results of change detection in Tokyo metropolitan area, Japan , using the Landsat TM data, and methods to quantify the ground cover classes. The changes are analyzed using the measures of not only conventional spectral classes but also a set of landscape indices to describe spatial properties of ground cove types using fractal dimension of objects, entropy in the specific windows defining the neighbors of focusing locations. In order eliminate the seasonal radiometric effects on TM data, an automated class labeling method is also attempted. Urban areas are also delineated automatically by defining the boundaries of the urban area. These procedures for urban change detection were implemented by the unified image computing methods proposed by the author, they can be automated in coherent and systematic ways, and it is anticipated to automate the whole procedures. The results of this analysis suggest that Tokyo metropolitan area was extended to the suburban areas along the new transportation networks and the high density area of Tokyo were also very much extended during the period between 1985 and 1995.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.135-137
• /
• 2003

The purpose of this preliminary study is the applied to geology using the Landsat TM satellite image of the Tanggung area of southern part of the Bandung, Indonesia to provide basic information for geological survey. For this, topography, geology and satellite image were constructed to spatial database. Digital elevation, slope, aspect, curvature, hill shade of topography were calculated from the topographic database and lithology was imported from the geological database. Lineament, lineament density, and NDVI were extracted the Landsat TM satellite image. The results showed the close relationship between geology and terrain and satellite image. Each sedimentary rock seldom corresponds with geology and analyses of topography but as a whole for sedimentary rocks coincide with them. Tuff and volcanic breccia in the volcanic rocks correspond with the result of terrain analyses. Talus deposits is well matched with the analyses of opography/satellite image.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1329-1331
• /
• 2003

Improvement of the classification accuracy is one of the major concerns in the field of remote sensing application research in recent years. Previous research shows that the accuracy of the conventional classification methods based only on the original spectral information were usually unsatisfied and need to be refined by manual edit. This present paper describes a method of combining the image processing, ancillary data (such as digital elevation model) and expert knowledge (especially the knowledge of local professionals) to improve the efficiency and accuracy of the satellite image classification in hilly land. Firstly, the Landsat TM data were geo-referenced. Secondly, the individual bands of the image were intensitynormalized and the normalized difference vegetation index (NDVI) image was also generated. Thirdly, a set of sample pixels (collected from field survey) were utilized to discover their corresponding DN (digital number) ranges in the NDVI image, and to explore the relationships between land use type and its corresponding spectral features . Then, using the knowledge discovered from previous steps as well as knowledge from local professionals, with the support of GIS technology and the ancillary data, a set of conditional statements were applied to perform the TM imagery classification. The results showed that the integration of image processing and spatial analysis functions in GIS improved the overall classification result if compared with the conventional methods.

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

• Journal of Industrial Technology
• /
• v.18
• /
• pp.17-25
• /
• 1998

Recently, there are a lot of studies to use a satellite image data in order to investigate a simultaneous change of a wide range area as a lake. However, many cases of a water quality research occur as problem when we try to extract the water quality factors from the satellite image data, because of the atmosphere scattering exert as bad influence on a result of analysis. In this study, and attempt was made to select the relative atmospheric correction method for the water quality factors extraction from the satellite image data. And also, the time-series analysis of the water quality factors extraction from the satellite image data. And also, the time-series analysis of the water quality factors was performed by using the multi-temporal image data.