• Atmosphere
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• v.17 no.2
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• pp.159-169
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• 2007

A comparison of the three land cover data sets (United States Geological Survey: USGS, International Geosphere Biosphere Programme: IGBP, and University of Maryland: UMd), derived from 1992-1993 Advanced Very High Resolution Radiometer(AVHRR) data sets, was performed over the Asian continent. Preprocesses such as the unification of map projection and land cover definition, were applied for the comparison of the three different land cover data sets. Overall, the agreement among the three land cover data sets was relatively high for the land covers which have a distinct phenology, such as urban, open shrubland, mixed forest, and bare ground (>45%). The ratios of triple agreement (TA), couple agreement (CA) and total disagreement (TD) among the three land cover data sets are 30.99%, 57.89% and 8.91%, respectively. The agreement ratio between USGS and IGBP is much greater (about 80%) than that (about 32%) between USGS and UMd (or IGBP and UMd). The main reasons for the relatively low agreement among the three land cover data sets are differences in 1) the number of land cover categories, 2) the basic input data sets used for the classification, 3) classification (or clustering) methodologies, and 4) level of preprocessing. The number of categories for the USGS, IGBP and UMd are 24, 17 and 14, respectively. USGS and IGBP used only the 12 monthly normalized difference vegetation index (NDVI), whereas UMd used the 12 monthly NDVI and other 29 auxiliary data derived from AVHRR 5 channels. USGS and IGBP used unsupervised clustering method, whereas UMd used the supervised technique, decision tree using the ground truth data derived from the high resolution Landsat data. The insufficient preprocessing in USGS and IGBP compared to the UMd resulted in the spatial discontinuity and misclassification.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Proceedings of the KSRS Conference
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• v.1
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• pp.426-429
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• 2006

Flood caused by Typhoon and severe rain during summer is the most destructive natural disasters in Korea. Almost every year flood has resulted in a big lost of national infrastructure and loss of civilian lives. It usually takes time and great efforts to estimate the flood-related damages. Government also has pursued proper standard and tool for using state-of-art technologies. High resolution satellite imagery is one of the most promising sources of ground truth information since it provides detailed and current ground information such as building, road, and bare ground. Once high resolution imagery is utilized, it can greatly reduce the amount of field work and cost for flood related damage assessment. The classification of high resolution image is pre-required step to be utilized for the damage assessment. The classified image combined with additional data such as DEM and DSM can help to estimate the flooded areas per each classified land use. This paper applied object-oriented classification scheme to interpret an image not based in a single pixel but in meaningful image objects and their mutual relations. When comparing it with other classification algorithms, object-oriented classification was very effective and accurate. In this paper, IKONOS image is used, but similar level of high resolution Korean KOMPSAT series can be investigated once they are available.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.41-47
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• 2019

In this paper, we propose a application of conditional generative adversarial network (cGAN) for generation of contrast enhanced computed tomography (CT) image. Two types of CT data which were the enhanced and non-enhanced were used and applied by the histogram equalization for adjusting image intensities. In order to validate the generation of contrast enhanced CT data, the structural similarity index measurement (SSIM) was performed. Prepared generated contrast CT data were analyzed the statistical analysis using paired sample t-test. In order to apply the optimized algorithm for the lymph node cancer, they were calculated by short to long axis ratio (S/L) method. In the case of the model trained with CT data and their histogram equalized SSIM were $0.905{\pm}0.048$ and $0.908{\pm}0.047$. The tumor S/L of generated contrast enhanced CT data were validated similar to the ground truth when they were compared to scanned contrast enhanced CT data. It is expected that advantages of Generated contrast enhanced CT data based on deep learning are a cost-effective and less radiation exposure as well as further anatomical information with non-enhanced CT data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3207-3213
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• 2011

Most methods of color constancy, which is the ability to determine the object color regardless of the scene illuminant, have failed to meet our expectation of their performance especially about low-illuminated scenes. Some methods with high performance need to be developed, but we must, above all else, obtain experimental images for analyzing the required circumstances or evaluating the methods. Therefore, the paper produces new sets of images so that they can be used in the development of color constancy methods suitable for low-illuminated scenes. These sets are composed of two parts: one part of images which are synthesized with spectral power distribution(SPD) of illuminants, spectral reflectance curve of reflectances, and sensor response functions of camera; the other part of images where the intensity of each image is adjusted at the uniform rate. In an experiment, the use of the sets takes an advantage that its result images are analyzed and evaluated quantitatively as their ground truth data are known in advance.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.321-332
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• 2024

Understanding the status of surface cover in riparian zones is essential for river management and flood disaster prevention. Traditional survey methods rely on expert interpretation of vegetation through vegetation mapping or indices. However, these methods are limited by their ability to accurately reflect dynamically changing river environments. Against this backdrop, this study utilized satellite imagery to apply the Random Forest method to assess the distribution of vegetation in rivers over multiple years, focusing on the Naeseong Stream as a case study. Remote sensing data from Sentinel-2 imagery were combined with ground truth data from the Naeseong Stream surface cover in 2016. The Random Forest machine learning algorithm was used to extract and train 1,000 samples per surface cover from ten predetermined sampling areas, followed by validation. A sensitivity analysis, annual surface cover analysis, and accuracy assessment were conducted to evaluate their applicability. The results showed an accuracy of 85.1% based on the validation data. Sensitivity analysis indicated the highest efficiency in 30 trees, 800 samples, and the downstream river section. Surface cover analysis accurately reflects the actual river environment. The accuracy analysis identified 14.9% boundary and internal errors, with high accuracy observed in six categories, excluding scattered and herbaceous vegetation. Although this study focused on a single river, applying the surface cover classification method to multiple rivers is necessary to obtain more accurate and comprehensive data.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.306-312
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• 2021

This paper describes localization of autonomous underwater vehicles(AUV), which can be used when some navigation sensor data are an outlier. In that situation, localization through existing navigation algorithms causes problems in long-range localization. Even if an outlier sensor data occurs once, problems of localization will continue. Also, if outlier sensor data is related to azimuth (direction of AUV), it causes bigger problems. Therefore, a parallel localization module, in which different algorithms are performed in a normal and abnormal situation should be designed. Before designing a parallel localization module, it is necessary to study an effective method in the abnormal situation. So, we propose a localization method through machine learning. For this method, a learning model consists of only Fully-Connected and trains through randomly contaminated real sea data. The ground truth of training is displacement between subsequent GPS data. As a result, average error in localization through the learning model is 0.4 times smaller than the average error in localization through the existing navigation algorithm. Through this result, we conclude that it is suitable for a component of the parallel localization module.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.61.3-62
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• 2015

Remote-sensing observation is one of the observation methods that provide valuable information, such as composition and surface physical conditions of solar system objects. The Hayabusa spacecraft succeeded in the first sample returning from a near-Earth asteroid, (25143) Itokawa. It has established a ground truth technique to connect between ordinary chondrite meteorites and S-type asteroids. One of the scientific observation instruments that Hayabusa carried, Asteroid Multi-band Imaging Camera(AMICA) has seven optical-near infrared filters (ul, b, v, w, x, p, and zs), taking more than 1400 images of Itokawa during the rendezvous phase. The reflectance of planetary body can provide valuable information of the surface properties, such as the optical aspect of asteroid surface at near zero phase angle (i.e. Sun-asteroid-observer's angle is nearly zero), light scattering on the surface, and surface roughness. However, only little information of the phase angle dependences of the reflectance of the asteroid is known so far. In this study, we investigated the phase angle dependences of Itokawa's surface to understand the surface properties in the solar phase angle of $0^{\circ}-40^{\circ}$ using AMICA images. About 700 images at the Hayabusa rendezvous phase were used for this study. In addition, we compared our result with those of several photometry models, Minnaert model, Lommel-Seeliger model, and Hapke model. At this conference, we focus on the AMICA's v-band data to compare with previous ground-based observation researches.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.734-738
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• 2002

We present spectral characteristics of tidal flat sediments and algal mat that were tested in the Gomso and Saemangum tidal flats, Korea. The objective of this study is to investigate the spectral reflectance and the radar scattering modeling in the tidal flats. Ground truth data obtained in the tidal flats include grain size, soil moisture content and its variation with time, surface roughness, chlorophyll, ground leveling, and field spectral reflectance measurement. The concept of an effective exposed area (EEA) is introduced to accommodate the effect of remnant surface water, and it seriously affects the reflection of short wavelength infrared and microwave. The nin size of 0.0625 mm has been normally used as a critical size of mud and sand discrimination. But we propose here that 0.25 mm is more practical grain size criterion to discriminate by remote sensing. Algal mat is the primary product in tidal flats, and it is found to be very important to understand spectral characteristics for tidal flat remote sensing. We have also conducted radar scattering modeling, and showed L-band HV-polarization would be the most effective combination.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.37-37
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• 2003

Rice is one of the most important crop in the world and is a major export of Thailand. Optical sensors are not useful for rice monitoring, because most cultivated areas are often obscured by cloud during the growing period, especially in South East Asia. Spaceborne Synthetic Aperture Radar (SAR) such as RADARSAT, can see through regardless of weather condition which make it possible to monitor rice growth and to retrieve rice acreage, using the unique temporal signature of rice fields. This paper presents the result of a study of examining the backscatter behavior of rice using multi-temporal RADARSAT dataset. Ground measurements of paddy parameters and water and soil condition were collected. The ground truth information was also used to identify mature rice crops, orchard, road, residence, and aquaculture ponds. Land use class distributions from the RADARSAT image were analyzed. Comparison of the mean DB of each land use class indicated significant differences. Schematic representation of temporal backscatter of rice crop were plotted. Based on the study carried out in Pathum Thani Province test site, the results showed variation of sigma naught from first tillering vegatative phase until ripenning phase. It is suggested that at least, three radar data acquisitions taken at 3 stages of rice growth circle namely; those are at the beginning of rice growth when the field is still covered with water, in the ear differentiation period, and at the beginning of the harvest season, are required for rice monitoring. This pilot project was an experimental one aiming at future operational rice monitoring and potential yield predicttion.