• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.90-100
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• 2017

The purpose of this study is to detect and label the vehicles using the drone images as a way to overcome the limitation of the existing point and section detection system and vehicle gap estimation on Arterial road. In order to select the appropriate time zone, position, and altitude for the acquisition of the drone image data, the final image data was acquired by shooting under various conditions. The vehicle was detected by applying mixed Gaussian, image binarization and morphology among various image analysis techniques, and the vehicle was labeled by applying Kalman filter. As a result of the labeling rate analysis, it was confirmed that the vehicle labeling rate is 65% by detecting 185 out of 285 vehicles. The gap was calculated by pixel unitization, and the results were verified through comparison and analysis with Daum maps. As a result, the gap error was less than 5m and the mean error was 1.67m with the preceding vehicle and 1.1m with the following vehicle. The gaps estimated in this study can be used as the density of the urban roads and the criteria for judging the service level.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.537-546
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• 2007

In hyperspectral remote sensing, linear spectral mixture model is a common procedure decomposing into the components of a mixed pixel and estimating the fraction of each end-member. Although linear spectral mixture model is frequently used in geology and mineral mapping because this model is simple and easy to apply, this model is not always valid in forest and urban area having rather complex structure. This study aims to analyze possible error for applying linear spectral mixture model. For the study, we measured laboratory spectra of mixture sample, having various materials, fractions, distributions. The accuracy of linear mixture model is low with the mixture sample having similar fraction because the multi-scattering between components is maximum. Additionally, this multi-scattering is related to the types, fraction, and distribution of components. Further analysis is necessary to quantify errors from linear spectral mixture model.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.121-135
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• 2014

Object-based classification methods provide a valid alternative to traditional pixel-based methods. This study reports the results of an object-based classification to examine land cover in the demilitarized zones(DMZs) of Inje-gun. We used land cover classes(7 classes for main category and 13 classes for sub-category) selected from the criteria by Korea Ministry of Environment. The average and standard deviation of the spectrum values, and homogeneity of GLCM were chosen to map land cover types in an hierarchical approach using the nearest neighborhood method. We then identified the distributional characteristics of land cover by considering 3 topographic characteristics (altitude, slope gradient, distance from the Southern Limited Line(SLL)) within the DMZs. The results showed that scale 72, shape 0.2, color 0.8, compactness 0.5 and smoothness 0.5 were the optimum weight values while scale, shape and color were most influenced parameters in image segmentation. The forests (92%) were main land cover type in the DMZs; the grassland(5%), the urban area (2%) and the forests (broadleaf forest: 44%, mixed forest: 42%, coniferous forest: 6%) also occupied mostly in land cover classes for sub-category. The results also showed that facilities and roads had higher density within 2 km from the SLL, while paddy, field and bare land were distributed largely outside 6 km from the SLL. In addition, there was apparent distinction in land cover by topographic characteristics. The forest had higher density at above altitude 600m and above slope gradient $30^{\circ}$ while agriculture, bare land and grass land were distributed mainly at below altitude 600m and below slope gradient $30^{\circ}$.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.543-557
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• 2021

Because aerosols have different spectral characteristics according to the size and composition of the particle and to the satellite sensors, a comparative analysis of aerosol products from various satellite sensors is required. In South Korea, however, a comprehensive study for the comparison of various official satellite AOD (Aerosol Optical Depth) products for a long period is not easily found. In this paper, we aimed to assess the performance of the AOD products from MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), Himawari-8, and Sentinel-3 by referring to the AERONET (Aerosol Robotic Network) sun photometer observations for the period between January 2015 and December 2019. Seasonal and geographical characteristics of the accuracy of satellite AOD were also analyzed. The MODIS products, which were accumulated for a long time and optimized by the new MAIAC (Multiangle Implementation of Atmospheric Correction) algorithm, showed the best accuracy (CC=0.836) and were followed by the products from VIIRS and Himawari-8. On the other hand, Sentinel-3 AOD did not appear to have a good quality because it was recently launched and not sufficiently optimized yet, according to ESA (European Space Agency). The AOD of MODIS, VIIRS, and Himawari-8 did not show a significant difference in accuracy according to season and to urban vs. non-urban regions, but the mixed pixel problem was partly found in a few coastal regions. Because AOD is an essential component for atmospheric correction, the result of this study can be a reference to the future work for the atmospheric correction for the Korean CAS (Compact Advanced Satellite) series.