• Atmosphere
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• v.20 no.4
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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.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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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

• Journal of Wetlands Research
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• v.13 no.2
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• pp.275-289
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• 2011

We investigated the hydrological and geomorphological characteristics of small irrigation ponds in civilian control zone of Paju city in Korea. Among 85 small irrigation ponds, water level of 52 ponds changed seasonally and that of 33 was constant. Water sources of 12 ponds were surface water, 29 surface water and ground water, and 44 ground water. 4 ponds locate in the edges of forests, 33 in flat-lands, and 48 in valleys. Water in 45 ponds was exchanged with paddy fields and 40 ponds were isolated from paddy fields. Endangered or endemic species were inhabited in 26 ponds, which have ground water as water source and constant water level. Based on these characteristics, we suggested 4 types of small irrigation ponds: spring, water exchanging, stagnant/spring, stagnant water. This classification system will help ecosystem managers to investigate ponds systematically and manage them based on pond type.

• Journal of Korean Society of Disaster and Security
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• v.10 no.1
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• pp.43-46
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• 2017

Recently, the development of underground space is being actively carried out in the urban area by saturation, and the excavation works are mainly carried out by various excavation methods by the structures adjacent to the ground and underground excavation. During such excavation work, ground subsidence accidents are occurring due to inattention construction, lack of construction technology, and leakage of ground water. For the prevention of ground subsidence we studied the method of risk influence factors by soil investigation. Analysis of 75 sites soil investigation by U.S.C.S (Unified Soil Classification System), construction method, depth of excavation and we studied the risk influence factors with ground subsidence.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.894-902
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• 2010

This paper presents a vision-based robust off-road terrain classification method against environmental variation. As a supervised classification algorithm, we applied a neural network classifier using wavelet features extracted from wavelet transform of an image. In order to get over an effect of overall image feature variation, we adopted environment sensors and gathered the training parameters database according to environmental conditions. The robust terrain classification algorithm against environmental variation was implemented by choosing an optimal parameter using environmental information. The proposed algorithm was embedded on a processor board under the VxWorks real-time operating system. The processor board is containing four 1GHz 7448 PowerPC CPUs. In order to implement an optimal software architecture on which a distributed parallel processing is possible, we measured and analyzed the data delivery time between the CPUs. And the performance of the present algorithm was verified, comparing classification results using the real off-road images acquired under various environmental conditions in conformity with applied classifiers and features. Experiments show the robustness of the classification results on any environmental condition.

• Economic and Environmental Geology
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• v.4 no.1
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• pp.33-37
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• 1971

Economical evaluation of an aquifer in an unconsolidated formation is based on the physiography, geology and hydraulics in it's loci. Since each foundation is controlled by the combination of several factors, these factors in each foundation will be explained in regard to their function, contributing to the yield of ground water from aquifers.

• Journal of the Korean Professional Engineers Association
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• v.24 no.3
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• pp.43-50
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• 1991

On the ground foundation works for Bldg site, Rock classification test can be obtained as follows due to the International Society for Rock Mechanics. 1. In-take test ; Compression strength, Point load test. 2. In-situ test : Schmidt hammer test. Burden test finaly the convinient co-relation table between strength and S.H. test were carried out for site-engineer. This project is one of contineous works regarding to Burden test from Jack leg drill( ø 36mm) to Crawler drill( ø 75mm) use.

• Explosives and Blasting
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• v.9 no.3
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• pp.10-16
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• 1991

On the ground foundation works for Bldg site, Rock classification test can be obtained as follows due to the International Society for Rock Mechanics. 1. In-situ test : Compressive strength, Point load test. 2. In-situ test Schmidt hammer test. Burden test finaly the convinient co-relation table between strength and 5. H, test were carried out for site-engineer, This project is one of contineous works regarding to Burden test from Jack leg drill($\phi{\;}75mm$) use.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.333-335
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• 2015

A computer vision-based scene recognition algorithm is proposed for monitoring construction sites. The system analyzes images acquired from a surveillance camera to separate regions and classify them as building, ground, and hole. Mean shift image segmentation algorithm is tested for separating meaningful regions of construction site images. The system would benefit current monitoring practices in that information extracted from images could embrace an environmental context.

• Journal of National Security and Military Science
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• s.1
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• pp.191-229
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• 2003

Multi-sensor data fusion techniques combine evidences from multiple sensors in order to get more accurate and efficient meaningful information through several process levels that may not be possible from a single sensor alone. One of the most important parts in the data fusion system is the identification fusion, and it can be categorized into physical models, parametric classification and cognitive-based models, and parametric classification technique is usually used in multi-sensor data fusion system by its characteristic. In this paper, we propose a novel heuristic identification fusion method in which we adopt desirable properties from not only parametric classification technique but also cognitive-based models in order to meet the realtime processing requirements.