• 대한원격탐사학회지
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• 제21권3호
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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.

• 터널과지하공간
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• 제4권2호
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• pp.170-185
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• 1994

Surface subsidence is one of the problems caused by mined out caverns. Depending on the geologic conditions and mining methods, subsidence can occur in various forms. This report describes the ground stability assessment for the mining induced subsidence area where unfilled caverns still exist abandoned. Geologic features which could affect the stability of the ground were investigated and all the possible geophysical methods were employed to obtain data that could explain the state of the ground in question. Basic rock tests were conducted from the drill cores and rock mass classification was performed by core logging and borehole camera investigation. Numerical analyses were carried out to predict the ground stability using data obtained by various investigations. The result could have been more reliable if in-situ stress were measure and reflected in the numerical analysis.

• Geomechanics and Engineering
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• 제37권5호
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• pp.467-474
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• 2024

Monitoring and managing the condition of underground utilities is crucial for ground stability. This study aims to determine whether images obtained using ground penetrating radar (GPR) accurately reflect the characteristics of buried pipelines through image analysis. The investigation focuses on pipelines made from different materials, namely concrete and steel, with concrete pipes tested under various diameters to assess detectability under differing conditions. A total of 400 images are acquired at locations with pipelines, and for comparison, an additional 100 data points are collected from areas without pipelines. The study employs GPR at frequencies of 200 MHz and 600 MHz, and image analysis is performed using machine learning-based convolutional neural network (CNN) techniques. The analysis results demonstrate high classification reliability based on the training data, especially in distinguishing between pipes of the same material but of different diameters. The findings suggest that the integration of GPR and CNN algorithms can offer satisfactory performance in exploring the ground's interior characteristics.

• 한국재료학회지
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• 제17권11호
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• pp.606-611
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• 2007

This study was conducted to beneficiation of magnesite by dry grinding and air classification. The raw ore was ground in a ball mill and pin mill controlled with grinding time and linear velocity of grinding media and fractionated in an air classifier. Pin mill is more efficient than the ball mill for liberation. As a result, the MgO grade of concentrate was 47.1% with recovery of 51.51% for classified with 3,000rpm of air classifier for ground at 13,000rpm in pin mill.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1377-1382
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• 2018

In the marine environment, many clutters have similar features with the marine targets due to the diverse changes of the air temperature, water temperature, various weather and seasons. Also, the clutters in the ground environment have similar features due to the same reason. In this paper, we proposed a robust Hybrid Machine Character (HMC) method to classify the targets from the clutters in the infrared images for the various environments. The proposed HMC method adopts human's multiple personality utilization and the CNN ensemble method to classify the targets in the ground and marine environments. This method uses an advantage of the each environmental training model. Experimental results demonstrate that the proposed method has better success rate to classify the targets and clutters than previously proposed CNN classification method.

• 대한공간정보학회지
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• 제16권2호
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• pp.23-29
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• 2008

본 연구에서는 도시 지상시설물을 보다 지능적이고 능동적으로 관리하기 위해 첨단 유비쿼터스 센서네트워크 기술을 도입함에 있어 가장 기본이 되는 체계적 시설물 분류와 이에 대한 분석을 다루고 있다. 그동안의 u-City 및 시설물 지능화 관련 선행연구를 통해 유비쿼터스 센서네트워크 기술 도입에 대한 필요성과 일부 응용사례 등이 발표된 바 있으나, 체계적인 시설 분류나 특성별 센서 도입 방안에 관한 연구는 미진한 실정이다. 이에 본 연구를 통해 기존 도시에서 관리하고 있는 지상 시설물들의 주요 관리특성을 도출하고, 이를 체계적으로 분류 및 분석함으로써 시설물관리 및 유비쿼터스 센서네트워크의 융합에 기여하고자 한다.

• Earthquakes and Structures
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• 제9권5호
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• pp.983-997
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• 2015

This research evaluates the soil conditions for seismic stations situated in Romania using the horizontal-to-vertical spectral ratio (HVSR). The strong ground motion database assembled for this study consists of 179 analogue and digital strong ground motion recordings from four intermediate-depth Vrancea seismic events with $M_w{\geq}6.0$. In the first step of the analysis, the influence of the earthquake magnitude and source-to-site distance on the H/V curves is evaluated. Significant influences from both the earthquake magnitude and hypocentral distance are found especially for soil class A sites. Next, a site classification method proposed in the literature is applied for each seismic station and the soil classes are compared with those obtained from borehole data and from the topographic slope method. In addition, the success and error rates of this method are computed and compared with other studies from the literature. A more in-depth analysis of the H/V results is performed using data from seismic stations in Bucharest and a comparison of the free-field and borehole H/V curves is done for three seismic stations. The results show large differences between the free-field and the borehole curves. As a conclusion, the results from this study represent an intermediary step in the evaluation of the soil conditions for seismic stations in Romania and the need to perform more detailed soil classification analysis is highly emphasized.

• 농촌계획
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• 제26권2호
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• pp.51-59
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• 2020

In this study, we analyzed the characteristics of 511 agricultural districts through statistical data, and classify these districts as the vulnerable area to drought through correlation and cluster analysis. The criteria for classification was related to ground-water recharge, irrigation water demand, and water supply. As a result, 8 types of agricultural districts were extracted. For example, the type 1 indicated the high priority area for ground-water development, thus the districts which were classified as type 1 showed ground-water use was less than 80 % of maximum capacity, and irrigation water supply was only 37.5 % and 76.5 % of irrigation water demand in upland and paddy field, respectively. As a result, 44 of 511 districts were classified as type 1.36 districts (types 5-8) were areas where groundwater development is limited. The results of this study are expected to provide useful information for establishing the direction of the rural area development project in connection with the revitalization of policy of people return to rural area.

• 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.

• 한국농공학회논문집
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• 제64권1호
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• pp.51-63
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• 2022

Up-to-date statistics of crop cultivation status is essential for farm land management planning and the advancement in remote sensing technology allows for rapid update of farming information. The objective of this study was to develop a classification model of rice paddy or winter crop fields based on NDWI, NDVI, and HSV indices using Sentinel-2 satellite images. The 18 locations in central Korea were selected as target areas and photographed once for each during summer and winter with a eBee drone to identify ground truth crop cultivation. The NDWI was used to classify summer paddy fields, while the NDVI and HSV were used and compared in identification of winter crop cultivation areas. The summer paddy field classification with the criteria of -0.195