• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.786-789
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• 2003

The detection of sandstorms and industrial pollutants has been the emphasis of this study. Data obtained from meteorological satellites, NOAA and GMS, have been used for detailed analysis. MODIS and Landsat images are also used for the application of future KOMPSAT- 2. Verification of satellite observations has been made with air pollution data obtained by ground-level monitors. It was found that satellite measurements agree well with concentrations and variations of air pollutants measured on the ground, and that satellite technique is a very useful device for monitoring large-scale air pollution in East Asia. The quantitative analysis of satellite image data on air pollution is the goal in the future studies.

• 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 Korea Water Resources Association
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• v.38 no.11
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• pp.927-935
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• 2005

Soil erosion by rainfall is important factor for basin management because it reduces reservoir capacity and breaks out the contamination of water caused by turbid water. Recently, soil erosion study with GIS is in progress but does not consider soil erosion source area. This study calculated soil erosion amount using GIS-based soil erosion model in Imha basin and examined soil erosion source area using SPOT 5 High-resolution satellite image and land cover map. As a result of analysis, dry field showed high-density soil erosion area and we could easily investigate source area using satellite image. Also we could examine the suitability of soil erosion area by applying field survey method in common areas such as dry field and orchard area those are difficult to confirm soil erosion source area using satellite image.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.357-360
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• 2005

Soil loss is widely recognized as a threat to farm livelihoods and ecosystem integrity worldwide. Soil loss prediction models can help address long-range land management planning under natural and agricultural conditions. Even though it is hard to find a model that considers all forms of erosion, some models were developed specifically to aid conservation planners in identifying areas where introducing soil conservation measures will have the most impact on reducing soil loss. Revised Universal Soil Loss Equation (RUSLE) computes the average annual erosion expected on hillslopes by multiplying several factors together: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practice (P). The value of these factors is determined from field and laboratory experiments. This study calculated soil erosion using GIS-based RUSLE model in Imha basin and examined soil erosion source area using SPOT 5 high-resolution satellite image and land cover map. As a result of analysis, dry field showed high-density soil erosion area and we could easily investigate source area using satellite image. Also we could examine the suitability of soil erosion area applying field survey method in common areas (dry field & orchard area) that are difficult to confirm soil erosion source area using satellite image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.235-242
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• 2002

This study is focused on the analysis of road extracting accuracy from the high resolution IKONOS satellite image data. A geometric correction of the image is performed using the RFM and interpretation with the screen digitizing is also performed for extracting the roads information. For the evaluation of road extracting accuracy, the road locations and the road widths are compared with the national digital map. The comparison results shows that the road boundary and the size of road width are able to extract with the geometric accuracy of $\pm$3.4m and $\pm$1.1m.

• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.47-54
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• 2012

Additional high resolution satellite images, other period or site, are essential for efficient city modeling and analysis. However, the same ground objects have a radiometric inconsistency in different satellite images and it debase the quality of image processing and analysis. Moreover, in an urban area, buildings, trees, bridges, and other artificial objects cause shadow effects, which lower the performance of relative radiometric normalization. Therefore, in this study, we exclude shadow areas and suggest the selective histogram matching methods for image based application without supplementary digital elevation model or geometric informations of sun and sensor. We extract the shadow objects first using adjacency informations with the building edge buffer and spatial and spectral attributes derived from the image segmentation. And, Outlier objects like a asphalt roads are removed. Finally, selective histogram matching is performed from the shadow masked multi-temporal Quickbird-2 images.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.218.2-218.2
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• 2012

In the remote sensing researches, the reflected bright source such as snow, cloud have effects on the image quality of wanted signal. Even though those signal from bright source are adjusted in corresponding pixel level with atmospheric correction algorithm or radiometric correction, those can be problem to the nearby signal as one of the stray light source. Especially, in the step and stare observational method which makes one mosaic image with several snap shots, one of target area can affect next to the other snap shot each other. Presented in this paper focused on the stray light analysis from unwanted reflected bright source for geostationary ocean color sensor. The stray light effect for total 16 slot images each other were performed according to 8 band filters. For the realistic simulation, we constructed system modeling with integrated ray tracing technique which realizes the same space time in the remote sensing observation among the Sun, the Earth, and the satellite. Computed stray light effect in the results of paper demonstrates the distinguishable radiance value at the specific time and space.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Journal of Astronomy and Space Sciences
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• v.28 no.4
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• pp.299-304
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• 2011

As more satellites are designed to downlink their observed image data through the X-band frequency band, it is inevitable that the occupied bandwidth of a target satellite will overlap with that of other X-band downlink satellites. For sun-synchronized low earth orbit satellites, in particular, it can be expected that two or more satellites be placed within the looking angle of a ground station antenna at the same time. Due to the overlapping in the frequency band, signals transmitted from the adjacent satellites act as interferers, leading to degraded link performance between target satellite and ground station. In this paper, link analysis was initiated by modeling the radiation pattern of ground station antenna through a validated Jet Propulsion Laboratory peak envelope model. From the relative antenna gain depending on the offset angle from center axis of maximum antenna directivity, the ratio of received interference signal level to the target signal level was calculated. As a result, it was found that the degradation increased when the offset angle was within the first point of radiation pattern. For a 7.3 m antenna, serious link degradation began at an offset angle of 0.4 degrees. From this analysis, the link performance of the coming satellite passes can be recognized, which is helpful to establish an operating procedure that will prevent the ground station from receiving corrupted image data in the event of a degraded link.