• 대한원격탐사학회지
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• 제6권1호
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• pp.11-24
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• 1990

This is the study about the meteorological satellite cloud image classification by objective methods. For objective cloud classification, linear discriminant analysis was tried. In the linear discriminant analysis 27 cloud characteristic parameters were retrieved from GMS infrared image data. And, linear cloud classification model was developed from major parameters and cloud type coefficients. The model was applied to GMS IR image for weather forecasting operation and cloud image was classified into 5 types such as Sc, Cu, CiT, CiM and Cb. The classification results were reasonably compared with real image.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.

• 천문학회보
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• 제38권2호
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• pp.49.1-49.1
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• 2013

We present a new galaxy classification scheme in the Wide-field Infrared Survey Explorer (WISE) [$3.4{\mu}m$]-[$12{\mu}m$] color versus $12{\mu}m$ luminosity diagram. In this diagram, galaxies can be classified into three groups in different evolutionary stages. Late-type galaxies are distributed linearly along "MIR star-forming sequence" identified by Hwang et al. (2012). Some early-type galaxies show another sequence at [3.4]-[12] $(AB){\simeq}-2.0$, and we call this 'MIR blue sequence'. They are quiescent systems with old stellar population older than 10 Gyr. Between the MIR star-forming sequence and the MIR blue sequence, some early- and late-type galaxies are sparsely distributed, and we call these galaxies 'MIR green cloud galaxies'. Interestingly, both MIR blue sequence galaxies and MIR green cloud ones lie on the red sequence in the optical color-magnitude diagram. However, MIR green cloud galaxies have lower stellar masses and younger stellar populations (smaller $D_n4000$) than MIR blue sequence galaxies, suggesting that MIR green cloud galaxies are in the transition stage from MIR star-forming sequence galaxies to MIR blue sequence ones. We present differences in various galaxy properties between the three MIR classes using a multi-wavelength data, combined with the WISE and Sloan Digital Sky Survey Data Release 10, of local (0.03 < z < 0.07) galaxies.

• 대기
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• 제17권2호
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• pp.125-133
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• 2007

Cloud amount calculation algorithm was developed using MTSAT-1R satellite data. The cloud amount is retrieved at 5 km ${\times}$ 5 km over the Korean Peninsula and adjacent sea area. The algorithm consists of three steps that are cloud detection, cloud type classification, and cloud amount calculation. At the first step, dynamic thresholds method was applied for detecting cloud pixels. For using objective thresholds in the algorithm, sensitivity test was performed for TBB and Albedo variation with temporal and spatial change. Detected cloud cover was classified into 3 cloud types (low-level cloud, cirrus or uncertain cloud, and cumulonimbus type high-level cloud) in second step. Finally, cloud amount was calculated by the integration method of the steradian angle of each cloud pixel over $3^{\circ}$ elevation. Calculated cloud amount was compared with measured cloud amount with eye at surface observatory for the validation. Bias, RMSE, and correlation coefficient were 0.4, 1.8, and 0.8, respectively. Validation results indicated that calculated cloud amount was a little higher than measured cloud amount but correlation was considerably high. Since calculated cloud amount has 5km ${\times}$ 5km resolution over Korean Peninsula and adjacent sea area, the satellite-driven cloud amount could show the possibility which overcomes the temporal and spatial limitation of measured cloud amount with eye at surface observatory.

• 대기
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• 제29권5호
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• pp.639-658
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• 2019

The classification of fog type and the characteristics of fog based on fog events over South Korea were investigated using a 3-year (2015~2017) visibility meter data. One-minute visibility meter data were used to identify fog with present weather codes and surface observation data. The concept of fog events was adopted for the better definition of fog properties and more objective classification through the detailed investigation of life cycle of fog. Decision tree method was used to classify the fog types and the final fog types were radiation fog, advection fog, precipitation fog, cloud base lowering fog and morning evaporation fog. We enhanced objectivity in classifying the types of fog by adding the satellite and the buoy observations to the conventional usage of AWS and ceilometer data. Radiation fog, the most common type in South Korea, frequently occurs in inland during autumn. A considerable number of advection fogs occur in island area in summer, especially in July. Precipitation fog accounts for more than a quarter of the total fog events and frequently occurs in islands and coastal areas. Cloud base lowering fog, classified using ceilometer, occurs occasionally for all areas but the occurrence rate is relatively high in east and west coastal area. Morning evaporation fog type is rarely observed in inland. The occurrence rate of thick fog with visibility less than 100 meters is amount to 21% of total fog events. Although advection fog develops into thick fog frequently, radiation fog shows the minimum visibility, in some cases.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.660-663
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• 2006

Rainfall estimation is important to weather forecast, flood control, hydrological plan. The empirical and statistical methods by measured data(surface rain gauge, rainfall radar, Satellite) is commonly used for rainfall estimation. In this study, the rainfall intensity for East Asia region was estimated using the empirical relationship between SSM/I data of DMSP satellite and brightness temperature of GEOS-9(10.7${\mu}m$) with cloud types(ISCCP and MSG classification). And the empirical formula for rainfall estimation was produced by PMM (Probability Matching Method).

• 대기
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• 제20권4호
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• pp.415-426
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• 2010

A Land cover map over East Asian region (Kongju national university Land Cover map: KLC) is classified by using support vector machine (SVM) and evaluated with ground truth data. The basic input data are the recent three years (2006-2008) of MODIS (MODerate Imaging Spectriradiometer) NDVI (normalized difference vegetation index) data. The spatial resolution and temporal frequency of MODIS NDVI are 1km and 16 days, respectively. To minimize the number of cloud contaminated pixels in the MODIS NDVI data, the maximum value composite is applied to the 16 days data. And correction of cloud contaminated pixels based on the spatiotemporal continuity assumption are applied to the monthly NDVI data. To reduce the dataset and improve the classification quality, 9 phenological data, such as, NDVI maximum, amplitude, average, and others, derived from the corrected monthly NDVI data. The 3 types of land cover maps (International Geosphere Biosphere Programme: IGBP, University of Maryland: UMd, and MODIS) were used to build up a "quasi" ground truth data set, which were composed of pixels where the three land cover maps classified as the same land cover type. The classification results show that the fractions of broadleaf trees and grasslands are greater, but those of the croplands and needleleaf trees are smaller compared to those of the IGBP or UMd. The validation results using in-situ observation database show that the percentages of pixels in agreement with the observations are 80%, 77%, 63%, 57% in MODIS, KLC, IGBP, UMd land cover data, respectively. The significant differences in land cover types among the MODIS, IGBP, UMd and KLC are mainly occurred at the southern China and Manchuria, where most of pixels are contaminated by cloud and snow during summer and winter, respectively. It shows that the quality of raw data is one of the most important factors in land cover classification.

• 제어로봇시스템학회논문지
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• 제17권5호
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• pp.436-443
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• 2011

Recognition of structures in urban environments is a fundamental ability for unmanned ground vehicles. In this paper we propose the geometrical featured voxel which has not only 3-D coordinates but also the type of geometrical properties of point cloud. Instead of dealing with a huge amount of point cloud collected by range sensors in urban, the proposed voxel can efficiently represent and save 3-D urban structures without loss of geometrical properties. We also provide an urban structure classification algorithm by using the proposed voxel and machine learning techniques. The proposed method enables to recognize urban environments around unmanned ground vehicles quickly. In order to evaluate an ability of the proposed map representation and the urban structure classification algorithm, our vehicle equipped with the sensor system collected range data and pose data in campus and experimental results have been shown in this paper.

• 한국환경과학회지
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• 제32권12호
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• pp.883-898
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• 2023

To investigate the occurrence characteristics and types of fog on the Korean Peninsula over the past three years (2020 to 2022), data from 96 synoptic meteorological observatories and 21 ocean buoys were collected and analyzed. We included precipitation fog, which occurs after precipitation events, and cloud-base lowering fog, which is caused by the development of lower-level clouds, with a total six subtypes of fog. In the case of cloud-base lowering fog, the occurrence frequency at 2.6% was not high at 2.6%, but the duration of low visibility below 200 m was very long at 6.9 hours. The seasonal frequency of fog is low in spring and winter, high in summer over islands and coastal areas, and high in autumn over inland areas. The frequency of inland fog, which is characterized by high radiation fog and dense fog, requires attention in terms of transportation safety, with an occurrence time of 0500 LST to 1000 LST. Therefore, systematic analysis of precipitation fog and cloud-base lowering, as well as radiation and advection fog, is required in the analysis of recognizing fog as a disaster and causing transportation disorders.

• 한국전산구조공학회논문집
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• 제34권6호
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• pp.355-362
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• 2021

증기운 폭발의 폭압을 예측하거나 위험성 분석을 위하여 다양한 폭압 산정법이 존재하지만 대표적으로 경험적 방법인 TNT 등가량 환산법과 멀티에너지법을 주로 사용한다. 멀티에너지법은 환경적 요인을 고려한 폭발강도계수를 사용한다. 본 연구에서는 문헌 분석을 통하여 점화원 강도를 세분하고 강도분류를 확장하여 개선한 폭발강도계수 가이드라인을 제안하였다. 개선한 폭발강도계수 가이드라인의 합리성 검증과 기존 Kinsella 가이드라인과의 비교를 위하여 실제 추정 폭압과 대조가 가능한 4가지의 증기운 폭발 사례를 적용하였다. 결과적으로 기존 Kinsella 가이드라인은 실제 추정 폭압에 비하여 광범위하거나 부정확한 폭압 산정 결과를 나타내는 것으로 확인하였다. 반면, 개선한 폭발강도계수 가이드라인은 명확한 점화원의 강도 선정이 가능하고 분류의 확장을 통하여 더욱 세분화된 계수 값의 선정이 가능함에 따라 실제 사례와 비교적 유사한 폭압 산정이 가능하다.