• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.140-148
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• 2001

The satellite sensor model is typically established using ground control points acquired by ground survey Of existing topographic maps. In some cases where the targeted area can't be accessed and the topographic maps are not available, it is difficult to obtain ground control points so that geospatial information could not be obtained from satellite image. The paper presents several satellite sensor models and satellite image decomposition methods for non-accessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then the behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in 1$^{st}$, 2$^{nd}$ and 3$^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\phi$(phi) correlated highly with positional parameters could be assigned to constant values. For non-accessible area, satellite images were decomposed, which means that two consecutive images were combined as one image. The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1$^{st}$ order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.471-478
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• 2011

High resolution Pan-sharpening technique is becoming increasingly important in the field of remote sensing image analysis as an essential image processing to improve the spatial resolution of original multispectral image. The general scheme of pan-sharpening technique consists of upsampling process of multispectral image and high-pass detail injection process using the panchromatic image. The upsampling process, however, brings about image blurring, and this lead to spectral distortion in the pan-sharpening process. In order to solve this problem, this paper presents a new method that adopts image restoration techniques based on optimization theory in the pan-sharpening process, and evaluates its efficiency and application possibility. In order to evaluate the effect of image restoration techniques on the pansharpening process, the result obtained using the existing method that used bicubic interpolation were compared visually and quantitatively with the results obtained using image restoration techniques. The quantitative comparison was done using some spectral distortion measures for use to evaluate the quality of pan-sharpened image.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.79-86
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• 2020

Purpose: In this study, a model was developed to estimate the storage in Cheonan reservoir using images taken by Sentinel-1 satellite. Method: A total of three reservoirs were studied. All three reservoirs are small reservoirs whose water level is being measured. The preprocessing of Sentinel-1 images was done using SNAP distributed by the European Space Agency(ESA), and the storage was estimated by classifying water surface by the threshold classification method. The estimated reservoir area was compared with satellite and drones images taken on the same day. The correlation was derived by comparing the estimated reservoir area with the actual measurement. Results and Conclusions: The storage values estimated by satellite image analysis showed similar values to the actual measurement data. However, because of the underestimation of the reservoir area due to green algae and Epilithic diatom of summer reservoirs and the low resolution of satellite images, it is dificult to detect reservoir area by satellite images less than 10,000㎡.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.321-327
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• 2008

In recently, researches which appling satellite image are introduced target to the forest area. Especially, it is satellite image analysis's advantage that collecting information of terrain at the direct accesses are dangerous and impassible area. But, the studies approaching to a inflectional paradigm of forests and change detection system for the distinguished forests type are leaves much to be desired. In this study, therefore, change of forests type was analyzed using Landsat TM satellite image which have multi-spectral bands. Furthermore, change detection system for forests type was constructed on web for the periodical monitoring.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.2 s.14
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• pp.101-110
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• 1999

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, in many cases of the water quality research there is one problem occured when extracting the water quality factors from the satellite image data because the atmosphere scattering exert a bad influence on a result of analysis. In this study, an attempt was made to select the relative atmospheric correction method, extract the water quality factors 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.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.215-224
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• 2007

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. This Study concludes, each image was measured by the rate of class separability, values classified were showed highly about $1,600{\sim}2,000$.

• Journal of Satellite, Information and Communications
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• v.9 no.3
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• pp.109-115
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• 2014

This paper suggest meteorological satellite processing software's structure that reduces time and efforts of modification/upgrade. This structure's key feature is "algorithm component" that works within framework and eventually to a complete Meteorological satellite processing system. Most of existing Meteorological satellite system is designed around specific function and data sets which limits range of modification and upgrade. In addition, re-use of current algorithms become difficult although re-use of similar algorithm is the case in many occasions. This inefficiency can be resolved by designing a new framework as a result of detail analysis of collected requirements. A new framework and system architecture has been designed. In addition, operational flow of Satellite image processing framework has been described.

• Journal of the Korean GEO-environmental Society
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• v.6 no.4
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• pp.73-80
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• 2005

The domestic Remote Sensing field uses mainly Landsat TM image that is used to the monitoring of the wide area. In this study, it is analyzed the land cover change of rural and urban area by time series using satellite images and is proposed the vision for a urban balanced development. It execute an analysis for urban change which is a fundamental data of city planning through the integration of the spatial analysis technique of GIS and Remote Sensing using satellite data.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.13-23
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• 2021

This study intended to assess the reliability of topographic data using satellite imaging data. The topographical data using actual instrumentation data and satellite image data were established and applied to the rainfall-leak model, S-RAT, and the topographical data and outflow data were compared and analyzed. The actual measurement data were collected from the Water Resources Management Information System (WAMIS), and satellite image data were collected from MODIS observation sensors mounted on Terra satellites. The areas subject to analysis were selected for two rivers with more than 80% mountainous areas in the Han River basin and one river basin with more than 7% urban areas. According to the analysis, the difference between instrumentation data and satellite image data was up to 50% for peak floods and up to 17% for flood totals in rivers with high mountains, but up to 13% for peak floods and up to 4% for flood totals. The biggest difference in the video data is Landuse, which shows that MODIS satellite images tend to be recognized as cities up to 60% or more in urban streams compared to WAMIS instrumentation data, but MODIS satellite images are found to be less than 5% error in forest areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.4
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• pp.1-9
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• 2014

The interest group and corporation in Korea have cultivated Suaeda grass in the source area every year as a plan to prevent the yellow dust due to Chinese desertification. It needs the afforestation analysis about the research area to plan the greenbelt environment development in the region of yellow dust origin. Thus, this research analyzed the greenbelt environment based on Landsat 5 TM satellite image and Landsat 8 image to grasp and analyze the present of greenbelt environment development. And this research analyzed the inside of the salt desert to understand the detailed greenbelt environment and vegetation index. As a result, it represents that the afforestation was accomplished efficiently between 2009 and 2011, while the greenbelt area was decreased rapidly and bare soil was increased between 2011 and 2013. Through these results, we could recognize that it is in trouble about the greenbelt environment development after 2011 and it needs the project implementation using satellite image when the next afforestation project is planned henceforth.