• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.359-360
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• 2021

The pixel-based processing of an image refers to a process of converting a value of one pixel only depending on the value of the current pixel, regardless of the value of another pixel. Pixel-based processing is used as the most basic operation in many fields such as image conversion, image enhancement, and image synthesis. There are processing methods such as arithmetic operation, histogram smoothing, and contrast stretching. In this paper, in order to clearly distinguish the tidal flat region from the tidal flat image of the west coast taken with a drone, we seek a method to find an efficient outline using pixel-based processing in the boundary detection part of the pre-processing process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.572-573
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• 2021

By optimizing the development of IoT-type thermal image-based photovoltaic fault detection equipment and interworking with drones using a drone with an intelligent path movement function, real-time analysis of the acquired image data facilitates fault reading of solar power plants. , design a system that can read out the failure of a solar panel using the image subtraction analysis technique and the presentation of the basic technology that can improve the power generation rate of the solar power plant and make an efficient maintenance model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.633-639
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• 2018

High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.179-184
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• 2021

Along with the development of the fourth industry, the public sector has increasingly paid more attention to search using drones and real-time monitoring, for various goals. The drones are used and researched to complete a variety of searching and monitoring missions, including search for missing persons, security, coastal patrol and monitoring, speed enforcement, highway and urban traffic monitoring, fire and wildfire monitoring, monitoring of illegal fishing in reservoirs and protest rally monitoring. Police stations, fire departments and military authorities, however, concentrate on the hardware part, so there are little research on efficient communication systems for the real-time monitoring of data collected from high-performance resolution and infrared thermal imagining cameras, and analysis programs suitable for special missions. In order to increase the efficiency of drones with the searching mission, this paper, therefore, attempts to propose an image analysis technique to increase the precision of search by producing image data suitable for searching missions, based on images obtained from drones and provide the foundation for improving relevant policies and establishing proper platforms, based on actual field cases and experiments.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.136-150
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• 2022

In this study, a comparative review was conducted on how to use RGB images to obtain river topographic information, which is one of the most essential data for eco-friendly river management and flood level analysis. In terms of the topographic information of river zone, to obtain the topographic information of flow section is one of the difficult topic, therefore, this study focused on estimating the river topographic information of flow section through RGB images. For this study, the river topography surveying was directly conducted using ADCP and RTK-GPS, and at the same time, and orthomosiac image were created using high-resolution images obtained by drone photography. And then, the existing developed regression equations were applied to the result of channel topography surveying by ADCP and the band values of the RGB images, and the channel bathymetry in the study area was estimated using the regression equation that showed the best predictability. In addition, CCHE2D flow modeling was simulated to perform comparative verification of the topographical informations. The modeling result with the image-based topographical information provided better water depth and current velocity simulation results, when it compared to the directly measured topographical information for which measurement of the sub-section was not performed. It is concluded that river topographic information could be obtained from RGB images, and if additional research was conducted, it could be used as a method of obtaining efficient river topographic information for river management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.97-102
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• 2017

Recently deep learning techniques such as convolutional neural networks (CNN) have been introduced to classify high-resolution remote sensing data. In this paper, we investigated the possibility of applying CNN to crop classification of farmland images captured by drones. The farming area was divided into seven classes: rice field, sweet potato, red pepper, corn, sesame leaf, fruit tree, and vinyl greenhouse. We performed image pre-processing and normalization to apply CNN, and the accuracy of image classification was more than 98%. With the output of this study, it is expected that the transition from the existing image classification methods to the deep learning based image classification methods will be facilitated in a fast manner, and the possibility of success can be confirmed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.41-49
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• 2022

Recently interests on the application of thermal cameras have increased with the advance of image analysis technology. Aside from a simple image acquisition, applications such as digital twin and thermal image management systems have gained popularity. To this end, we studied the effect of emissivity on the DN (Digital Number) value in the process of derivation of a relational expression for converting DN to an actual surface temperature. The DN value is a number representing the spectral band value of the thermal image, and is an important element constituting the thermal image data. However, the DN value is not a temperature value indicating the actual surface temperature, but a brightness value indicating high and low heat as brightness, and has a non-linear relationship with the actual surface temperature. The reliable relationship between DN and the actual surface temperature is critical for a thermal image processing. We tested the relationship between the actual surface temperature and the DN value of the thermal image, and then the radiation adjustment was performed to better estimate actual surface temperatures. As a result, the relation graph between the actual surface temperature and the DN value similarly show linear pattern with the relation graph between the radiation-controlled non-contact thermometer and the DN value. And the non-contact temperature after adjusting the emissivity was closer to the actual surface temperature than before adjusting the emissivity.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.3
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• pp.162-168
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• 2017

Shallow-water bathymetry survey has been conducted using high definition color images obtained at the altitude of 100 m above sea level using a drone. Shallow-water bathymetry data are one of the most important input data for the research of beach erosion problems. Especially, accurate bathymetry data within closure depth are critically important, because most of the interesting phenomena occur in the surf zone. However, it is extremely difficult to obtain accurate bathymetry data due to wave-induced currents and breaking waves in this region. Therefore, optical remote sensing technique using a small drone is considered to be attractive alternative. This paper presents the potential utilization of image processing algorithms using multi-variable linear regression applied to red, green, blue and grey band images for estimating shallow water depth using a drone with HD camera. Optical remote sensing analysis conducted at Wolpo beach showed promising results. Estimated water depths within 5 m showed correlation coefficient of 0.99 and maximum error of 0.2 m compared with water depth surveyed through manual as well as ship-board echo-sounder measurements.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.372-378
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• 2020

Drones, which show strong growth in the fourth industry, are commonly used for disaster response. In the event of an actual disaster, local governments should carry out 13 cooperative functions to respond to the disaster, but there are difficulties in collecting on-site information in places where it is difficult for people to access or grasp the situation. Based on the 13 collaborative functions that are essential for operations in the event of a disaster, a utilization plan of highly utilized drones was derived. Through the analysis of overseas cases and drone utilization by each department, a total of 10 out of 13 collaborative functions of the Disaster and Safety Countermeasures Headquarters (disaster situation management, emergency life safety support, emergency recovery, disaster resource support, traffic measures, medical and quarantine services, disaster site environment maintenance, social order maintenance, search, rescue, and emergency) were derived. These results can enhance the efficiency of the disaster response by presenting a plan to utilize drone images for each function.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1385-1386
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• 2015

본 논문은 실내 실험 환경에서 카메라에서 얻어진 영상정보를 캐니 경계선 검출 알고리즘을 적용하여 정지 상태인 미니 드론의 경계선을 검출하고 이를 기반으로 좌표를 인식한다. 캐니 알고리즘의 임계값에 따른 검출 결과의 변화를 확인한다.