• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.148-153
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• 1999

Baikdu-san was a very active volcano during the Cenozoic era and is believed to be formed in late Cenozoic era. Recently it was also reported that there was a major eruption in or around 1002 A.D. and there are evidences which indicate that it is still an active volcano and a potential volcanic hazard. Remote sensing techniques have been widely used to monitor various natural hazards, including volcanic hazards. However, during an active volcanic eruption, volcanic ash can basically cover the sky and often blocks the solar radiation preventing any use of optical sensors. Synthetic aperture radar(SAR) is an ideal tool to monitor the volcanic activities and lava flows, because the wavelength of the microwave signal is considerably longer that the average volcanic ash particle size. In this study we have utilized several sets of SAR data to evaluate the utility of the space-borne SAR system. The data sets include JERS-1(L-band) SAR, and RADARSAT(C-band) data which included both standard mode and the ScanSAR mode data sets. We also utilized several sets of auxiliary data such as local geological maps and JERS-1 OPS data. The routine preprocessing and image processing steps were applied to these data sets before any attempts of classifying and mapping surface geological features. Although we computed sigma nought ($\sigma$$^{0}$) values far the standard mode RADARSAT data, the utility of sigma nought image was minimal in this study. Application of various types of classification algorithms to identify and map several stages of volcanic flows was not very successful. Although this research is still in progress, the following preliminary conclusions could be made: (1) sigma nought (RADARSAT standard mode data) and DN (JERS-1 SAR and RADARSAT ScanSAR data) have limited usefulness for distinguishing early basalt lava flows from late trachyte flows or later trachyte flows from the old basement granitic rocks around Baikdu-san volcano, (2) surface geological structure features such as several faults and volcanic lava flow channels can easily be identified and mapped, and (3) routine application of unsupervised classification methods cannot be used for mapping any types of surface lava flow patterns.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.335-340
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• 2013

This paper has designed and realized educational ZigBee equipment befitting to the USN environment. In addition, this study has enabled users to exercise operation process for software technology education and to propose software design methods in the process in the USN environment through practice equipment for ZigBee education. As for the development environment of system, Atmega128 process of Atmel is used for CPU; AVR compiler for the debugging environment; C language for firmware development language; and C++ for application program. The system operation process is initiated by coordinator's sensing information reading order from the hyper terminal through a server through the Internet or directly connected; and then delivering it to a terminating device by using ZigBee technology. The terminating device delivers various sensing information to the coordinator which delivers it to a server through the Internet or to a HYPER terminal directly connected to the coordinator. As for the educational course, it is about practices on such ZigBee operation process and relevant programing skills. Regarding it, the communication between coordinator and terminating device is designed by utilizing physical layer of ZigBee protocol, MAC layer and network layer while the communication between server and coordinator is designed by proposing an independent protocol on TCP/IP socket and the protocol processing procedure during sensing data delivery is verified by interpretation.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.149-160
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• 2014

Recently studies on application development and utilization using sensors and devices embedded in smartphones have flourished at home and abroad. This study aimed to analyze the accuracy of the images of smartphone to determine three-dimension position of close objects prior to the development of photogrammetric system applying smartphone and evaluate the feasibility to use. First of all, camera calibration was conducted on autofocus and infinite focus. Regarding camera calibration distortion model with balance system and unbalance system was used for the decision of lens distortion coefficient, the results of calibration on 16 types of projects showed that all cases were in RMS error by less than 1 mm from bundle adjustment. Also in terms of autofocus and infinite focus on S and S2 model, the pattern of distorted curve was almost the same, so it could be judged that change in distortion pattern according to focus mode is very little. The result comparison according to autofocus and infinite focus and the result comparison according to a software used for multi-image processing showed that all cases were in standard deviation less than ${\pm}3$ mm. It is judged that there is little result difference between focus mode and determination of three-dimension position by distortion model. Lastly the checkpoint performance by total station was fixed as most probable value and the checkpoint performance determined by each project was fixed as observed value to calculate statistics on residual of individual methods. The result showed that all projects had relatively large errors in the direction of Y, the direction of object distance compared to the direction of X and Z. Like above, in terms of accuracy for determination of three-dimension position for a close object, the feasibility to use smartphone camera would be enough.

• Applied Chemistry for Engineering
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• v.19 no.2
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• pp.222-227
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• 2008

In this work we have studied the antifouling properties of the hydrophobic sol-gel modified sensing membrane and its optical properties for sensor application. E. coli JM109, B. cereus 318 and P. pastoris X-33 were cultivated in confocal cultivation dishes with glass surface, respectively. The glass surface was coated with the hydrophobic sol-gels prepared by the dimethoxy-dimethyl-silane (DiMe-DMOS) and tetramethyl-orthosilicate (TMOS). After cultivation, microorganisms adhered on the surface coated with sol-gels and glass surface were dyed by gram-staining method and the numbers of microorganisms were analyzed based on the image data of the scanning electronic microscope (SEM). A great number of microorganisms, about $2{\sim}3{\times}10^4/mm^2$, was adhered on the glass surfaces which no hydrophobic sol-gels were coated. However, the antifouling effect of the hydrophobic sol-gels was large, that microorganisms of less than $200{\sim}300/mm^2$ were adhered on the coated glass surface. The performance of the sensing membranes for detection of pH and dissolved oxygen was enhanced by recoating the light insulation layer prepared with the mixture of the hydrophobic sol-gel and graphite particles.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2018.06a
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• pp.142.2-142.2
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• 2018

One-dimensional metal oxide nanostructures have attracted considerable research activities owing to their strong application potential as components for nanosize electronic or optoelectronic devices utilizing superior optical and electrical properties. In which, semiconducting $SnO_2$ material with wide-bandgap Eg = 3.6 eV at room temperature, is one of the attractive candidates for optoelectronic devices operating at room temperature [1, 2], gas sensor [3, 4], and transparent conducting electrodes [5]. The synthesis and gas sensing properties of semiconducting $SnO_2$ nanomaterials have become one of important research issues since the first synthesis of SnO2 nanowires. In this study, $SnO_2$ nanowire networks were synthesized on a basis of a two-step process. In step 1, Sn spheres (30-800 nm in diameter) embedded in $SiO_2$ on a Si substrate was synthesized by a chemical vapor deposition method at $700^{\circ}C$. In step 2, using the source of these Sn spheres, $SnO_2$ nanowire (20-40 nm in diameter; $1-10{\mu}m$ in length) networks on a spherical Sn surface were synthesized by a thermal oxidation method at $800^{\circ}C$. The Au layers were pre-deposited on the surface of Sn spherical and subsequently oxidized Sn surface of Sn spherical formed SnO2 nanowires networks. Field emission scanning electron microscopy and high-resolution transmission electron microscopy images indicated that $SnO_2$ nanowires are single crystalline. In addition, the $SnO_2$ nanowire is also a tetragonal rutile, with the preferred growth directions along [100] and a lattice spacing of 0.237 nm. Subsequently, the $NO_2$ sensing properties of the $SnO_2$ network nanowires sensor at an operating temperature of $50-250^{\circ}C$ were examined, and showed a reversible response to $NO_2$ at various $NO_2$ concentrations. Finally, details of the growth mechanism and formation of Sn spheres and $SnO_2$ nanowire networks are also discussed.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.337-342
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• 2019

This letter describes the development of WRI (Wildfire Risk Index) using GDAPS (Global Data Assimilation and Prediction System) and satellite data, and its application to the Goseong-Sokcho and Gangneung-Donghae wildfires in April 4, 2019. We made sure that the proposed WRI represented the change of wildfire risk of around March 19 and April 4 very well. Our approach can be a viable option for wildfire risk monitoring, and future works will be necessary for the utilization of GK-2A products and the coupling with the wildfire prediction model of the Korea Forest Service.