• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.479-490
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• 2003

This study proposed a modified anisotropic diffusion restoration for image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. The gradient function involves a constant called "temperature", which determines the amount of discontinuity and is continuously decreased in the iterations. In this study, the proposed method has been extensively evaluated using simulated images that were generated from various patterns. These patterns represent the types of natural and artificial land-use. The simulated images were restored by the modified anisotropic diffusion technique, and then classified by a multistage hierarchical clustering classification. The classification results were compared to them of the non-restored simulation images. The restoration with an appropriate temperature considerably reduces error in classification, especially for noisy images. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1110-1127
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• 2022

To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.35-38
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• 2009

The paper presents an effective method to detect fire in video surveillance and monitoring system. The main contribution of this work is that we successfully use the Hidden Markov Models in the process of detecting the fire with a few preprocessing steps. First, the moving pixels detected from image difference, the color values obtained from the fire flames, and their pixels clustering are applied to obtain the image regions labeled as fire candidates; secondly, utilizing massive training data, including fire videos and non-fire videos, creates the Hidden Markov Models of fire and non-fire, which are used to make the final decision that whether the frame of the real-time video has fire or not in both temporal and spatial analysis. Experimental results demonstrate that it is not only robust but also has a very low false alarm rate, furthermore, on the ground that the HMM training which takes up the most time of our whole procedure is off-line calculated, the real-time detection and alarm can be well implemented when compared with the other existing methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.582-586
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• 2020

국가기후기술정보시스템은 국내 환경기술과 국외의 수요기술 정보를 제공하는 검색 시스템이다. 그러나 기존의 시스템은 유사한 뜻을 가진 단일 단어와 복수 단어들을 모두 식별하지 못하기에 유의어를 입력했을 경우 검색 결과가 다르다. 이런 문제점을 해결하기 위해 본 연구에서는 유의어 사전을 기반으로한 환경기술 검색 시스템을 제안한다. 이 시스템은 Word2vec 모델과 HDBSCAN(Hierarchical Density-Based Spatial Clustering of Application with Noise) 알고리즘을 이용해 유의어 사전을 구축한다. Word2vec 모델을 이용해 한국어와 영어 위키백과 코퍼스에 대해 형태소 분석을 진행한 후 단일 단어와 복수 단어를 포함한 단어를 추출하고 벡터화를 진행한다. 그 다음 HDBSCAN 알고리즘을 이용해 벡터화된 단어를 군집화 해주고 유의어를 추출한다. 기존의 Word2vec 모델이 모든 단어 간의 거리를 계산하고 유의어를 추출하는 과정과 대비하면 시간이 단축되는 역할을 한다. 추출한 유의어를 통합해 유의어 사전을 구축한다. 국가기후기술정보시스템에서 제공하는 국내외 기술정보, 기술정보 키워드와 구축한 유의어 사전을 Multi-filter를 제공하는 Elasticsearch에 적용해 최종적으로 유의어를 식별할 수 있는 환경기술 검색 시스템을 제안한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.1
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• pp.25-33
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• 2020

A drought event is characterized by duration, severity and affected area. In general, after calculating a drought index using hydro-meteorological time series at a station, a drought event is defined based on the run theory to identify the beginning and end time. However, this one-dimensional analysis has limitations for analyzing the spatio-temporal occurrence characteristics and movement paths of drought. Therefore, this study is to define a three-dimensional drought event using a simple clustering algorithm and to develop a drought map that can be used to understand the drought severity according to the spatio-temporal expansion of drought. As a result, compared with the two-dimensional monitoring information to show spatial distribution of drought index, a proposed drought map is able to show three-dimensional drought characteristics inclusing drought duration, spatial cumulative severity, and centroid of drought. The analysis of drought map indicated that there was a drought event which had the affected area less than 10 % while on occations while there were 11 drought events (44 %) which had the affected area more a than 90 % of the total area. This means that it is important to understand the relationship between spatial variation of drought affected area and severity corresponding to various drought durations. The development of drought map based on three-dimensional drought analysis is useful to analyze the spatio-temporal occurrence characteristics and propagation patterns of regional drought which can be utilized in developing mitigation measures for future extreme droughts.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.307-316
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• 2009

In this paper, the spatiotemporal data mining methodology for detecting a cycle of power consumption pattern with the change of time and spatial was proposed, and applied to the power consumption data collected by GIS-AMR system with an aim to use its resulting knowledge in real world applications. First, partial clustering method was applied for cluster analysis concerned with the aim of customer's power consumption. Second, the patterns of customer's power consumption data which contain time and spatial attribute were detected by 3D cube mining method. Third, using the calendar pattern mining method for detection of cyclic patterns in the various time domains, the meanings and relationships of time attribute which is previously detected patterns were analyzed and predicted. For the evaluation of the proposed spatiotemporal data mining, we analyzed and predicted the power consumption patterns included the cycle of time and spatial feature from total 266,426 data of 3,256 customers with high power consumption from Jan. 2007 to Apr. 2007 supported by the GIS-AMR system in KEPRI. As a result of applying the proposed analysis methodology, cyclic patterns of each representative profiles of a group is identified on time and location.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.86-96
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• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.

• Journal of the Korean association of regional geographers
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• v.7 no.3
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• pp.107-120
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• 2001

This study is concerned with providing a reliable procedure of calibrating a set of places specific distance parameters and with applying it to U.S. inter-State migration flows between 1985 and 1900. It attempts to conform to recent advances in quantitative geography that are characterized by an integration of ESDA(exploratory spatial data analysis) and local statistics. ESDA aims to detect the spatial clustering and heterogeneity by visualizing and exploring spatial patterns. A local statistic is defined as a statistically processed value given to each location as opposed to a global statistic that only captures an average trend across a whole study region. Whereas a global distance parameter estimates an averaged level of the friction of distance, place-specific distance parameters calibrate spatially varying effects of distance. It is presented that a poisson regression with an adequately specified design matrix yields a set of either origin-or destination-specific distance parameters. A case study demonstrates that the proposed model is a reliable device of measuring a spatial dimension of migration, and that place-specific distance parameters are spatially heterogeneous as well as spatially clustered.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.175-181
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• 2014

Korean government proposed a new initiative 'government 3.0' with which the administration will open its dataset to the public before requests. City of Seoul is the front runner in disclosure of government data. If we know what kind of attributes are governing factors for any given segmentation, these outcomes can be applied to real world problems of marketing and business strategy, and administrative decision makings. However, with respect to city of Seoul, selection of optimal variables from the open dataset up to several thousands of attributes would require a humongous amount of computation time because it might require a combinatorial optimization while maximizing dissimilarity measures between clusters. In this study, we acquired 718 attribute dataset from Statistics Korea and conducted an analysis to select the most suitable variables, which differentiate Gangnam from other districts, using the Genetic algorithm and Dunn's index. Also, we utilized the Microsoft Azure cloud computing system to speed up the process time. As the result, the optimal 28 variables were finally selected, and the validation result showed that those 28 variables effectively group the Gangnam from other districts using the Ward's minimum variance and K-means algorithm.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.158-158
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• 2017

비닐포장 하부에 위치한 콩의 생장 초기에 발생한 초엽을 인식하기 위한 연구를 수행중이다. 선행 연구에서 비닐포장에 접촉한 콩 초엽으로 인해 비닐포장 상부 표면의 열 반응 분포에 변화가 있음을 발견하였다. 현장에서 주행 중에 콩 초엽의 위치를 실시간으로 인식하고 연동된 선형 또는 회전형 엑츄에이터를 제어하여 정확한 위치에 천공을 수행하기 위해서는 계측 시스템과 제어 시스템간의 시간적 차이를 최소할 수 있는 실시간 신호 처리 기술이 필수적이다. 선행 연구에서 사용한 다중 IR 센서의 분해능은 $16{\times}4pixel$이며 주파수는 3 Hz로, 폭이 30cm 내외인 비닐포장 상부의 정밀 분석에 한계가 있음을 발견하였다. 이를 해결하기 위하여 분해능과 계측 주기를 개선할 수 있는 초소형 ($1cm{\times}1cm{\times}1cm$) 열화상 센서를 이용하였다. LWIR(Longwave infrared)영역에 해당하는 $8{\mu}m{\sim}14{\mu}m$의 영역에서 $0.05^{\circ}C$의 분해능을 보이는 $ Lepton^{TM}$ (500-0690-00, FLIR, Goleta, CA)모델을 사용하였다. 프레임당 $80{\times}60$ 픽셀의 정보가 2 Byte의 단위로 계측이 되며 9 Hz의 주파수로 대상면의 열 분포를 측정할 수 있다. 이론적으로 초당 정보 전송량은 86,400 Byte ($80{\times}60{\times}2{\times}9$)이며, 1 m를 진행하는 주행형 천공기에 적용할 경우 1 프레임당 10cm 정도의 면적을 측정하므로, 최대 위치 판정 분해능은 약 10 cm / 60 pixel = 0.17 cm/pixel로 상대적으로 정밀한 위치 판별이 가능하다. $80{\times}60{\times}2Byet$의 정보를 0.1초 이내에 분석해야 하는 기술적 과제를 해결하기 위하여 천공 작업기에 적합한 상용 SBC(Single board computer)의 클럭 속도(1 Ghz)로 처리 가능한 공간 분포 분석 알고리즘을 개발하였다. 전체 이미지 도메인을 한 번에 분석하는데 소요되는 시간을 최소화하기 위하여 공간정보 행렬을 균등히 배분하고 별도의 프로세서에서 Feature를 분석한 후 개별 프로세서의 결과를 경합식으로 판정하는 기술을 연구하였다. 오픈 소스인 MPICH(www.mpich.org) 라이브러리를 이용하여 개발한 신호 분석 프로그램을 클러스터링으로 연동된 개별 코어에 설치/수행 하였다. 2D 행렬인 열분포 정보를 공간적으로 균등 분배하여 개별 코어에서 행렬의 Spatial domain analysis를 수행하였다. $20{\times}20$의 클러스터링 단위를 이용할 경우 총 12개의 코어가 필요하였으며, 초당 10회의 연산이 가능함을 확인하였다. 병렬 클러스터링 기술을 이용하여 1m/s 내외의 주행 속도에 대응이 가능한 비닐포장 상부 열 분포 분석 시스템을 구현하였다.