• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1791-1799
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• 2023

The Korea Aerospace Research Institute is responsible for supplying and supporting the utilization of imagery data from the Arirang satellite series for organizations affiliated with the Government Satellite Information Application Consultation. Most of them primarily utilize optical imagery, and there is a relative lack of utilization of Synthetic Aperture Radar (SAR) imagery. In this paper, as part of supporting the use of SAR images, we investigated SAR intensity-based change detection algorithms and their use cases that have been researched to determine SAR intensity-based change detection algorithms to be developed in the future. As a result of the research, we found that various algorithms utilizing intensity difference, correlation coefficients, histograms, or polarimetric information have been researched by numerous researchers to detect and analyze change pixels and the applications of change detection algorithms have been studied in various fields such as a city, flood, forest fire, and vegetation. This study will serve as a reference for the development of SAR change detection algorithms, intended for utilization in the Government Satellite Information Application Consultation.

• Journal of Korean Society for Geospatial Information Science
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• v.9 no.1 s.17
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• pp.19-25
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• 2001

Satellite remote sensing, with its synoptic coverage, is used to evaluate the trophic state of large reservoir for agriculture in Kum River basin. The prediction model for chlorophyll-${\alpha}$ which was derived from Daecheong reservoir was applied to four LANDSAT TM imageries to generate a distribution map of trophic state. The chlorophyll-${\alpha}$ model was found to be reasonably reliable predictors for average trophic state value of reservoir. LANDSAT TM imagery data appears to have great utility in assessing trophic state of reservoir.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.505-508
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• 2005

Wavelet scheme can be applied to the various remote sensing problems: conventional multi-resolution image analysis, compression of large image sets, fusion of heterogeneous sensor image and segmentation of features. In this study, we attempted wavelet-based filtering and its analysis. Traditionally, statistical methods and adaptive filter are used to manipulate noises in the image processing procedure. While we tried to filter random noise from optical image and radar image using Discrete Wavelet Transform (DW1) and Stationary Wavelet Transform (SW1) and compared with existing methods such as median filter and adaptive filter. In result, SWT preserved boundaries and reduced noises most effectively. If appropriate thresholds are used, wavelet filtering will be applied to detect road boundaries, buildings, cars and other complex features from high-resolution imagery in an urban environment as well as noise filtering

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.437-444
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• 2017

Since land pixels often generate false alarms in ship detection using Synthetic Aperture Radar (SAR), land masking is a necessary step which can be processed by a land area map or water database. However, due to the continuous coastline changes caused by newport, bridge, etc., an updated data should be considered to mask either the land or the oceanic part of SAR. Furthermore, coastal concrete facilities make noise signals, mainly caused by side lobe effect. In this paper, we propose two methods. One is a semi-automatic water body data generation method that consists of terrain correction, thresholding, and median filter. Another is a dynamic land masking method based on water database. Based on water database, it uses a breadth-first search algorithm to find and mask noise signals from coastal concrete facilities. We verified our methods using Sentinel-1 SAR data. The result shows that proposed methods remove maximum 84.42% of false alarms.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.161-166
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• 2003

Hyperspectral imagery have high spectral resolution and provide the potential for more accurate and detailed information extraction than any other type of remotely sensed data. In this paper, the "Linear Spectral Unmixing" model which is one solution to overcome the limit of spatial resolution for remote sensing data was introduced and we applied the algorithm to hyperspectral image. The result was not good because of some problems such as image calibration and used endmembers. Therefore, we analyzed the cause and had a search for a solution.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.163-168
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• 1998

Extracting a Digital Elevation Model (DEM) from spaceborne imagery is important for cartographic applications of remote sensing data. The procedure for such DEM generation can be divided into stereo matching, sensor modelling and DEM interpolation. Among these, DEM interpolation contributes significantly to the completeness and accuracy of a DEM and, yet, this technique is often considered "trivial". However, na\ulcornere DEM interpolation may result in a less accurate and sometimes meaningless DEM. This paper reports the performance analysis of various DEM interpolation techniques. Using a manually derived DEM as reference, a number of sample points were created randomly. Different interpolation techniques were applied to the sample points to generate DEMs. The performance of interpolation was assessed by the accuracy of such DEMs. The results showed that kriging gave the best results at all times whereas nearest neighborhood interpolation provided a fast solution with moderate accuracy when sample points were large enough.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.43-48
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• 2001

Many applications in the areas of agricultural, hydrological and environmental resource management require data over very large areas and with a high imaging frequency - monitoring crop growth, water stress, seasonal wetland flooding and natural vegetation development. This precludes the use of fine resolution data (Landsat, Spot) on the grounds of cost, accessibility and low imaging frequency. Meteorological satellites have the potential to fill this need, given their very wide spatial coverage, and high repeat imaging. The Remote Sensing Unit (RSU) at the Zambia Meteorological Department routinely receives, processes and archives imagery from both Meteosat and NOAA AVHRR satellites. Here I wish to present some examples of applications of these data sets that arise from the RSU work - relationships between rainfall and vegetation development as assessed by satellite, derived information and seasonal patterns of flooding in the Barotse floodplain and the Kafue flats. I also wish to outline ways in which a more widespread use of this data by the Zambian institutions canbe achieved.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.4-6
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• 2003

Optical signals from an object may vary at different conditions caused by differences in light source and sensor position. Knowledge of these variations is necessary to enable calibration of the satellite images and confirmation of the sun and sensor angles influences of the spectral signals from the objects. With the use high -resolution Ikonos$^{TM}$ multi-angular images, the bi- directional reflectance effects of mangrove trees were observed when three datasets were compared. The influence of bi- directional reflectance may affect the accuracy of interpreting satellite imagery and obtaining biophysical parameters mangrove and other vegetation by indirect means.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.609-611
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• 2003

In the past, it has always been time -consuming and labor intensive to extract and update Digital Elevation Model (DEM). How to extract highly accurate DEM with efficiently and the most economical method has always been a cutting-edge topic in the remote sensing filed. This paper discusses using PCI Geomatica OrthoEngine software to extract DEM automatically from ASTER stereo satellite images (15 m resolution). For the study, DEMs were extracted for two sites in Taiwan, and the resulting DEMs were found to have RMS errors between 10 and 16 meters in both flat and mountainous areas.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.287-294
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• 2009

KOMPSAT-2 like other high-resolution satellites has the time and angle difference in the acquisition of the panchromatic (PAN) and multispectral (MS) images because the imaging systems have the offset of the charge coupled device combination in the focal plane. Due to the differences, high altitude and moving objects, such as clouds, have a different position between the PAN and MS images. Therefore, a mis-registration between the PAN and MS images occurs when a registration algorithm extracted matching points from these cloud objects. To overcome this problem, we proposed a new registration method. The main idea is to discard the matching points extracted from cloud boundaries by using an automatic thresholding technique and a classification technique on a distance disparity map of the matching points. The experimental result demonstrates the accuracy of the proposed method at ground region around cloud objects is higher than a general method which does not consider cloud objects. To evaluate the proposed method, we use KOMPSAT-2 cloudy images.