• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.213-218
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• 2008

동일 지점을 촬영한 위성영상은 위성의 센서나 영상의 취득 시기, 지형의 상태 등에 따라 그 지점에 나타나는 화소값이 일정하지 않다. 이러한 영상은 영상간 모자이크나 변화 탐지 결과에 영향을 미칠 가능성이 높으므로 방사보정(또는 방사정규화)을 통해 화소값의 차이를 최소화시킬 필요가 있다. 본 연구는 선형회귀식을 적용한 상대 방사정규화에 초점을 맞추고 있으며, 선형회귀식 구성에 필요한 PIF(Pseudo Invariant Feature)를 자동으로 추출하기 위해 Ordinal Rank 알고리즘을 적용하였다. 이 방법을 통해 각 밴드별 후보 PIF를 추출하고, 공통으로 해당되는 최종 PIF를 추출할 수 있었다. RMSE(Root Mean Square Error), Dynamic range, Coefficient of variation 등을 통해 방사보정 후의 결과를 평가해보았다. 영상회귀를 이용한 방사보정알고리즘과의 비교를 통해 제안된 알고리즘이 갖는 장점을 확인하였다.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.309-313
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• 2009

산불은 전세계적으로 발생하는 가장 주요한 재해현상 중 하나이다. 산불 감시나 산불에 의한 피해지역의 효과적인 관측은 피해 지역을 최소화하고, 효율적인 피해 복구 계획 수립에 매우 중요한 기초자료를 제공한다. 광학 위성 자료를 활용한 산불 피해지역 탐지가 널리 사용되고 있음에도 불구하고, 산불에 의한 연기 또는 구름 분포에 의해 종종 사용상에 제약이 있다. 본 연구에서는 2000년 4월 강원도 고성, 강릉, 삼척, 물진 지역에서 발생한 대규모 산불을 연구 대상지역으로 하여, 1998년-2000년 동안 획득된 RADARSAT-1 SAR 영상을 이용하여 산불 피해 지역 감시의 활용성을 연구하였다. 산불에 의한 산림 피해지역 관측을 위해 RADARSAT-1 SAR 영상의 후방산란관의 변화를 통한 변환 탐지를 수행하였다. 산불 피해지역에서 산불 전에 비해 산불 후에 획득된 RADARSAT-1 SAR 영상의 후방산란값이 증가하는 것으로 관측되었다. RADARSAT-1 SAR 영상으로부터 관측된 산불 피해 지역은 Landsat-7 ETM 자료와 현장 조사 자료에 의한 산불 피해 지역과 매우 상관성이 높은 것으로 관측되었다.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1075-1087
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• 2017

In this study, we tried to utilize results of the change detection analysis for satellite images as the basis for updating the land cover map. The Sequential Spectral Change Vector Analysis ($S^2CVA$) was applied to multi-temporal multispectral satellite imagery in order to extract changed areas, efficiently. Especially, we minimized the false alarm rate of unsupervised change detection due to the seasonal variation using the direction information in $S^2CVA$. The binary image, which is the result of unsupervised change detection, was integrated with the existing land cover map using the zonal statistics. And then, object-based analysis was performed to determine the changed area. In the experiment using PlanetScope data and the land cover map of the Ministry of Environment, the change areas within the existing land cover map could be detected efficiently.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.583-598
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• 2015

This paper presents derivation of background temperature from geostationary satellite and its validation based on ground measurements and Geographic Information System (GIS) for future use in weather and surface heat variability. This study only focuses on daily and monthly brightness temperature in 2012. From the analysis of COMS Meteorological Data Processing System (CMDPS) data, we have found an error in cloud distribution of model, which used as a background temperature field, and in examining the spatial homogeneity. Excessive cloudy pixels were reconstructed by statistical reanalysis based on consistency of temperature measurement. The derived Brightness temperature has correlation of 0.95, bias of 0.66 K and RMSE of 4.88 K with ground station measurements. The relation between brightness temperature and both elevation and vegetated land cover were highly anti-correlated during warm season and daytime, but marginally correlated during cold season and nighttime. This result suggests that time varying emissivity data is required to derive land surface temperature.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.325-331
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• 2008

We propose an algorithm to detect large Cochlodinium polykrikoides red tide event that was appeared in Korean coastal waters. This algorithm is based on two-stage filtering using MODIS chlorophyll information. Most of the red tide detection studies generally use assumption that sea water having high chlorophyll concentration is red tide events because of high correlation and red tide. However, these methods generate many commission errors such as turbid water by detecting inactive sea water of red tide. Therefore, we eliminated commission errors by applying two stage filtering and verified the algorithm's effectiveness by detecting large Cochlodinium polykrikoides red tide event that was appeared in Korean coastal waters.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1209-1216
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• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.267-277
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• 2022

Due to the increasing proportion of cloud and remote working environments, various information security incidents are occurring. Insider threats have emerged as a major issue, with cases in which corporate insiders attempting to leak confidential data by accessing it remotely. In response, insider threat detection approaches based on machine learning have been developed. However, existing machine learning methods used to detect insider threats do not take biases and variances into account, which leads to limited performance. In this paper, boosting-type ensemble learning algorithms are applied to verify the performance of malicious insider detection, conduct a close analysis, and even consider the imbalance in datasets to determine the final result. Through experiments, we show that using ensemble learning achieves similar or higher accuracy to other existing malicious insider detection approaches while considering bias-variance tradeoff. The experimental results show that ensemble learning using bagging and boosting methods reached an accuracy of over 98%, which improves malicious insider detection performance by 5.62% compared to the average accuracy of single learning models used.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.263-267
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• 2015

current mine detector can't division the section if it is conducted and it needs too much labor force and time. in addition to, if the user don't move the head of sensor in regular speed or move it too fast, it is hard to detect a mine exactly. according to this, to improve the problem using one direction ultrasonic wave sensing signal, that is made up of human body antenna part, main micro processor unit part, smart glasses part, body equipped LCD monitor part, wireless data transmit part, belt type power supply part, black box type camera, Security Communication headset. the user can equip this at head, body, arm, waist and leg in removable type. so it is able to detect the powder in a 360-degree on(under) the ground whether it is metal or nonmetal and it can express the 2D or 3D film about distance, form and material of the mine. so the battle combats can avoid the mine and move fast. also, through the portable battery and twin self power supply system of the power supply part, combat troops can fight without extra recharge and we can monitoring the battle situation of distant place at the command center server on real-time. and then, it makes able to sharing the information of battle among battle combats one on one. as a result, the purpose of this study is researching a smart wearable mine detector which can establish a smart battle system as if the commander is in the site of the battle.

• Journal of Korean Society for Geospatial Information Science
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• v.1 no.1 s.1
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• pp.193-201
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• 1993

In a number of areas, there are important benefits to be gained when we bring both the detection and monitoring abilities of remote sensing as well as the philosophical approach and analytic capabilities of a geographic information system to bear on a problem. A key area in the joint applications of remote sensing technology and GIS is to identify change. Whether this change is of interest for its own sake, or because the change causes us to act (for example, to update a map), remote sensing provides an excellent suite of tools for detecting change. At the same time, a GIS is perhaps the best analytic toot for quantifying the process of change. There are two alternative methods for quantifying changes. The conceptually simple approach is to un the pixel values in each of the images. This method is practical but may be too simple to identify the variety of changes in a complex scene. The common alternative is called symbolic change detection. The analyst first decides on a set of thematic categories that are important to distinguish for the application. This approach is useful only if accurate landuse/cover classifications can be obtained. Persons conducting digital change detection must be intimately familiar with the environment under study, the quality of the data set and the characteristics of change detection algorithms. Also, much work remains to identify optimum change detection algorithms for specific geographic areas and problems.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.493-502
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• 2021

Recently, because of changes in temperature and rising water temperatures due to increased pollution sources, many algae have been produced in the water system. Therefore, there has been a lot of research using satellite images for the generation and monitoring of green algae. However, in prior studies, it is difficult to consider the optical properties of the local water system by using only a single index, and by using medium and low-resolution satellite images to conduct large-scale algae detection, there is a problem of accuracy in narrow, broad rivers. Therefore, in this work, we utilize high-resolution images of Sentinel-2 satellites to perform green algae detection on a single index (NDVI, SEI, FGAI) and development index (NDVI & SEI, FGAI & SEI) that mixes single indices. In this study, POD, FAR, and PC values were utilized to evaluate the accuracy of green algae detection algorithms, and the FGAI & SEI index showed the highest accuracy with 98.29% overall accuracy PC.