• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.88-90
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• 2017

현재 한국해양과학기술원에서는 선박비행체 탑재용 복합센서를 개발 및 시험 적용 중에 있다. 그러나 얻어진 영상 데이터를 통해서는 목표물에 대한 정확한 위치 정보를 파악할 수 없다. 또한 크기가 큰 물체도 거리가 멀면 영상에선 작아 보이기 때문에 목표물의 크기 또한 파악하기 힘들다. 이를 보완하기 위해 본 연구에서는 복합센서를 통해 획득한 영상에 대해 warping 및 기하보정, 선박 및 익수자 자동 탐지 알고리듬, 위치 및 계수 정보 산출에 대해 소개한다. 또한 실제 실험을 통해 해당 알고리듬을 검증하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10c
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• pp.442-444
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• 2004

본 연구는 핸드헬드 디바이스와 레고 블럭을 이용하여 공장의 무인화와 실시간문제 탐지, 실시간 원격제어를 위한 무선기반의 자동화 공장을 위한 시뮬레이터 구현을 목표로 하였다. 이를 위해, PDA와 무선랜 기술을 이용하여 무선 공장 자동화모델을 시뮬레이션 하였으며, 원거리에서도 탐지 및 제어를 할 수 있으며 또한 발생되는 문제점을 미리 파악하고, 보다 정확한 공정관리 및 제어를 위해 시뮬레이션 모델을 설계 및 구현하였다. 향후, 이는 임베디드 리눅스뿐 아니라 WinCE 를 탑재한 휴대폰에서도 공정제어, 모니터링 등의 제어가 가능할 것으로 예측된다.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1223-1232
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• 2017

This paper presents an approach to detect changes caused by urban development over a large area using KOMPSAT optical images. In order to minimize the radiometric dissimilarities between the images acquired at different times, we apply the grid-based rough radiometric correction as a preprocessing to detect changes in a large area. To improve the accuracy of the change detection results for urban development, we mask-out non-interest areas such as water and forest regions by the use of land-cover map provided by the Ministry of Environment. The Change Vector Analysis(CVA) technique is applied to detect changes caused by urban development. To confirm the effectiveness of the proposed approach, a total of three study sites from Sejong City is constructed by combining KOMPSAT-2 images acquired on May 2007 and May 2016 and a KOMPSAT-3 image acquired on March 2014. As a result of the change detection accuracy evaluation for the study site generated from the KOMPSAT-2 image acquired on May 2007 and the KOMPSAT-3 image acquired on March 2014, the overall accuracy of change detection was about 91.00%. It is demonstrated that the proposed method is able to effectively detect urban development changes in a large area.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.601-611
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• 2011

Land cover changes are occurring for a variety of reasons such as urbanization, infrastructure construction, desertification, drought, flood, and so on. Many researchers have studied the cause and effect of land cover changes, and also the methods for change detection. However, most of the detection methods are based on the dichotomy of "change" and "not change" according a threshold value. In this paper, we present a change detection method with the integration of probability, spatial autocorrelation, and hotspot detection. We used the AMOEBA (A Multidirectional Ecotope-Based Algorithm) and developed the AMOEBA-CH (core hotspot) because the original algorithm tends to produce too many clusters. Our method considers the probability of land cover changes and the spatial interactions between each pixel and its neighboring pixels using a local spatial autocorrelation measure. The core hotspots of land cover changes can be delineated by a contiguity-dominance model of our AMOEBA-CH method. We tested our algorithm in a simulation for land cover changes using NDVI (Normalized Difference Vegetation Index) data in South Korea between 2000 and 2008.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.819-829
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• 2019

In the disaster of collapse, an immediate response is needed to prevent the damage from worsening, and damage area calculation, response and recovery plan should be established. This requires accurate detection of the damage affected area. This study performed the detection of the damaged area by using UAV which can respond quickly and in real-time to detect the collapse accident. The study area was selected as B-05 housing redevelopment area in Jung-gu, Ulsan, where the demolition of houses and apartments in progress as the redevelopment project began. This area resembles a collapsed state of the building, which clear changes before and after the demolition. UAV images were acquired on May 17 and July 9, 2019, respectively. The changing area was considered as the damaged area before and after the collapse of the building, and the changing area was detected using CVA (Change Vector Analysis) the Representative Change Detection Technique, and SLIC (Simple Linear Iterative Clustering) based superpixel algorithm. In order to accurately perform the detection of the damaged area, the uninterested area (vegetation) was firstly removed using ExG (Excess Green), Among the objects that were detected by change, objects that had been falsely detected by area were finally removed by calculating the minimum area. As a result, the accuracy of the detection of damaged areas was 95.39%. In the future, it is expected to be used for various data such as response and recovery measures for collapse accidents and damage calculation.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1317-1328
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• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.

• Journal of Navigation and Port Research
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• v.27 no.1
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• pp.97-102
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• 2003

Wire ropes are used in a myriad of various industrial applications such as elevator, mine hoist, construction machinery, lift, and suspension bridge. Especially, the wire rope of crane is important component to container transfer. If it happens wire rope failures during the operation, it may lead to safety accident, economic loss by productivity decline and so on. To solve this problem, we developed remote wire rope fault detecting system, and this system is consisted of 3 parts that portable fault detecting part, signal processing part and remote monitoring part. All detected signal has external noise or disturbance according to circumstances. So, we applied to discrete wavelet transform to extract a signal from noisy data. It is verified that the detecting system by de-noising has good efficiency for inspecting faults of wire ropes in service. As a result, by developing this system, container terminal could reduce expense because of extension fo wire ropes exchange period and could competitive power. Also, this system is possible to apply in several field such as elevator, lift and so on.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1685-1695
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• 2021

To generate a deep learning model with high performance, a large training dataset should be required. However, it requires a lot of time and cost to generate a large training dataset in remote sensing. Therefore, the importance of transfer learning of deep learning model using a small dataset have been increased. In this paper, we performed transfer learning of trained model based on open datasets by using orthoimages and digital maps to detect changes of building objects in multitemporal orthoimages. For this, an initial training was performed on open dataset for change detection through the HRNet-v2 model, and transfer learning was performed on dataset by orthoimages and digital maps. To analyze the effect of transfer learning, change detection results of various deep learning models including deep learning model by transfer learning were evaluated at two test sites. In the experiments, results by transfer learning represented best accuracy, compared to those by other deep learning models. Therefore, it was confirmed that the problem of insufficient training dataset could be solved by using transfer learning, and the change detection algorithm could be effectively applied to various remote sensed imagery.

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.26-33
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• 2017

A drone performs a mission through remote control or automatic control, which uses wireless communications technology. Recently the increasing use of drones, the drone signal RF detection is necessary. In this paper, we propose an efficient dron RF detection method through simulations considering Wi-Fi, Bluetooth and dedicated protocol dron communication method in ISM(Industry Science Medical) band.. After configuring an environment where a common terminal and a drone signal are mixed, a general terminal and a drone signal are distinguished from each other by using a RF characteristic according to a dron movement. The proposed drone RF detection method is the WRMD(Windowed RSSI Moving Detection) operation and the Doppler frequency identification method. The simulation environments consist to mixed for two signals and four signals. We analysis the performance to proposed drone RF detection technique thorough detection rate.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.04a
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• pp.147-148
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• 2002

기상과 환경 분야에서 원격 탐사의 중요성이 부각되고 있다. 레이저를 사용하여 원격 탐사를 수행하는 라이다는 대기 중으로 쏘아올린 레이저 빛이 대기 중 에어러솔이나 기체 분자에 의해 후방산란 된 신호를 측정한다(원재광, 1998). 이러한 라이다를 응용하여 탐지 대상의 공간적 분포와 연속적인 탐사를 통한 시간 변화를 획득할 수 있다. 극저출력(/고반복율) 레이저를 사용하여 망막안전을 보장하고 스캔시스템과 결합된 마이크로 펄스 라이다가 국내기술로 개발되어 에어러솔 관측에 활용되었으며, 향후 여러 분야에서 응용될 것으로 기대된다. (중략)