• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.707-723
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• 2020

In this study, a decision tree type of quality control (QC) method was developed to improve the temporal-spatial representation and accuracy of the visibility data being operated by the Korea Meteorological Administration (KMA). The quality of the developed QC method was evaluated through the application to the 3 years (2016.03-2019.02) of 290 stations visibility data. For qualitative and quantitative verification of the developed QC method, visibility and naked-eye data provided by the KMA and QC method of the Norwegian Meteorological Institute (NMI) were used. Firstly, if the sum of missing and abnormal data exceeds 10% of the total data, the corresponding point was removed. In the 2nd step, a temporal continuity test was performed under the assumption that the visibility changes continuously in time. In this process, the threshold was dynamically set considering the different temporal variability depending on the visibility. In the 3rd step, the spatial continuity test was performed under the assumption of spatial continuity for visibility. Finally, the 10-minute visibility data was calculated using weighted average method, considering that the accuracy of the visibility meter was inversely proportional to the visibility. As results, about 10% of the data were removed in the first step due to the large temporal-spatial variability of visibility. In addition, because the spatial variability was significant, especially around the fog area, the 3rd step was not applied. Through the quantitative verification results, it suggested that the QC method developed in this study can be used as a QC tool for visibility data.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.11-20
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• 2014

For Web-based applications in IoT environment, IETF CoRE WG has standardizing the CoAP. One of limitations of CoAP is that CoAP standard does not consider the mobility management of the CoAP sensor node. In this paper, we propose the mobility management protocol of CoAP sensor node by considering the characteristics of the constrained network. The proposed mobility management protocol supports for Web client to be transmitted the sensing data from CoAP node reliably while the CoAP sensor moves into different wireless networks. To do this, we designed the architecture with the separate IP address management of CoAP sensor node and presented the mobility management protocol, which includes the holding and binding mode, in order to provide the reliable transmission. Finally, the numerical analysis and simulation with NS2 tool have been done for the performance evaluation in terms of the handover latency and packet loss with comparing the proposed mobility management protocol with other the existing mobility management protocols. The performance result shows that the proposed mobility management can provide the transmission of sensing data without the packet loss comparing with the existing mobility management protocol reliably.

• Journal of Sensor Science and Technology
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• v.7 no.4
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• pp.254-262
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• 1998

Multilayer Langmuir-Blodgett (LB) films coated on quartz crystal microbalance (QCM) of octa-substituted metallophhtalocyanines ($MPc(OEH)_8$, M = Cu, Co, and Sn) and dihydrogen phthalocyanines ($H_2Pc(OEH)_8$) were used to quantify $NO_2$ concentrations. They were exposed to various concentrations of $NO_2$ in dry $N_2$. Among the four phthalocyanines we tested, the metal-free $H_2Pc(OEH)_8$ was observed to be most sensitive to $NO_2$. However, its LB film showed a partially irreversible behavior, that is part of the frequency change due to $NO_2$ adsorption could not be recovered even after purging with pure $N_2$ gas for an extended period. Examining the spectra of NMR and FTIR revealed fact that the irreversible portion of frequency change was due to ether groups in the linkage between side chains and the Pc unit. In order to remove the effect of such initial deactivation, on $NO_2$ quantification experiment, a freshly fabricated LB film was treated at a high concentration of $NO_2$ so that the ether sites were saturated. A pretreated LB film showed a reproducible performance for repeated uses. The $CuPc(OEH)_8$ LB film showed a satisfactory sensing performance down to as low as 4 ppm. For the $H_2Pc(OEH)_8$ LB film, the lower detection limit was found to be 35ppb of $NO_2$. In order to make the experimental condition more realistic, the carrier gas, dry nitrogen, was replaced by air. It was observed that the presence of oxygen, a weak electron acceptor, reduced the sensitivity and thus increased the sensing limit to hundreds of ppb. Results of experiments with moisture added showed that the interference of moisture was quite severe.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.333-338
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• 2017

We compared sea ice concentration(SIC) and sea ice extent(SIE) using EUMETSAT Ocean and Sea Ice Satellite Application Facilities(OSI SAF) and NASA Team(NT) sea ice algorithm in the Arctic during 1980-2010 to investigate the difference between sea ice data applied different algorithms. SIC and SIE of the two data showed different consistency by season and by sea area. Seasonally, SIC of OSI SAF was 0.85 % overall, 0.48 % in spring, 0.97 % in summer, 1.38 % in autumn and 0.66 % in winter higher than NT SIC. By sea area, OSI SAF SIC was 2.7 %, SIE was $198,000km^2$ higher than NT in Arctic Ocean, but in Lincoln Sea, OSI SAF SIC was 2.3 %, SIE was $20,000km^2$ lower than NT.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.303-316
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• 2005

Strong radar returns were observed in oyster sea farms, and coherent interferometric pairs were successfully constructed. Tide height in coastal area is possible to be measured by using interferometric phase and intensity of SAR data. This SAR application technique for measuring the tide height in the near coastal zone can be further improved when applied to double bounce dominant areas. In this paper, we investigate the characteristics of polarimetric signature in the oyster farm structures. Laboratory experiments were carried out using Ku-band according to the target scale. Radar returns from vertical poles are stronger than those from horizontal Pole by 10.5 dB. Single bounce components were as strong as double bounce components and more sensitive to antenna look direction. Double bounce components show quasi-linear relation with the height of vertical poles, which implies double bounce is more useful to determine water level than total power. A L-band NASA/IPL airborne SAR (AIRSAR) image was classified into single-, double-bounce, and volume scattering components. It is observed that oyster farms are not always characterized by double bounced scattering. Double bounce is a main scattering mechanism in oyster farms standing above seawater, while single bounce is stronger than double bounce when bottom tidal flats are exposed to air. Ratios of the normalized single to double bounce components in the former and latter cases were 0.46 and 5.62, respectively. It is necessary to use double bounce dominant sea farms for tide height measurement by DInSAR technique.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.621-626
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• 2006

Today's commercial high resolution satellite imagery such as IKONOS and QuickBird, offers the potential to extract useful spatial information for geographical database construction and GIS applications. Extraction of 3D building information from high resolution satellite imagery is one of the most active research topics. There have been many previous works to extract 3D information based on stereo analysis, including sensor modelling. Practically, it is not easy to obtain stereo high resolution satellite images. On single image performance, most studies applied the roof-bottom points or shadow length extracted manually to sensor models with DEM. It is not suitable to apply these algorithms for dense buildings. We aim to extract 3D building information from a single satellite image in a simple and practical way. To measure as many buildings as possible, in this paper, we suggested a new way to extract building height by triangular vector structure that consists of a building bottom point, its corresponding roof point and a shadow end point. The proposed method could increase the number of measurable building, and decrease the digitizing error and the computation efficiency.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.893-903
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• 2018

The necessity of automatic precise georeferencing is increasing with the increase applications of high-resolution satellite imagery. One of the methods for collecting ground control points (GCPs) for precise georeferencing is to use chip images obtained by extracting a subset of an image map such as an ortho-aerial image, and can be automated using an image matching technique. In this case, the importance of the image matching success rate is increased due to the limitation of the number of the chip images for the known reference points such as the unified control point. This study aims to propose a method to improve the success rate of image matching between KOMPSAT-3A images and GCP chip images from aerial ortho-images. We performed the image matching with 7 cases of band pair using KOMPSAT-3A panchromatic (PAN), multispectral (MS), pansharpened (PS) imagery and GCP chip images, then compared matching success rates. As a result, about 10-30% of success rate is increased to about 40-50% when using PS imagery by using PAN and MS imagery. Therefore, using PS imagery for image matching of KOMPSAT-3A images and aerial ortho-images would be helpful to improve the matching success rate.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1383-1398
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• 2018

Sea surface wind is one of the most fundamental variables for understanding diverse marine phenomena. Although scatterometers have produced global wind field data since the early 1990's, the data has been used limitedly in oceanic applications due to it slow spatial resolution, especially at coastal regions. Synthetic Aperture Radar (SAR) is capable to produce high resolution wind field data. KOMPSAT-5 is the first Korean satellite equipped with X-band SAR instrument and is able to retrieve the sea surface wind. This study presents the validation results of sea surface wind derived from the KOMPSAT-5 backscattering coefficient data for the first time. We collected 18 KOMPSAT-5 ES mode data to produce a matchup database collocated with buoy stations. In order to calculate the accurate wind speed, we preprocessed the SAR data, including land masking, speckle noise reduction, and ship detection, and converted the in-situ wind to 10-m neutral wind as reference wind data using Liu-Katsaros-Businger (LKB) model. The sea surface winds based on XMOD2 show root-mean-square errors of about $2.41-2.74m\;s^{-1}$ depending on backscattering coefficient conversion equations. In-depth analyses on the wind speed errors derived from KOMPSAT-5 backscattering coefficient data reveal the existence of diverse potential error factors such as image quality related to range ambiguity, discrete and discontinuous distribution of incidence angle, change in marine atmospheric environment, impacts on atmospheric gravity waves, ocean wave spectrum, and internal wave.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.695-706
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• 2022

Medium and high-resolution optical satellites have proven their effectiveness in detecting wildfire areas. However, smoke plumes generated by wildfire scatter visible light incidents on the surface, thereby interrupting accurate monitoring of the area where wildfire occurs. Therefore, a technology to extract smoke in advance is required. Deep learning technology is expected to improve the accuracy of smoke extraction, but the lack of training datasets limits the application. However, for clouds, which have a similar property of scattering visible light, a large amount of training datasets has been accumulated. The purpose of this study is to develop a smoke extraction technique using deep learning, and the limits due to the lack of datasets were overcome by using a cloud dataset on transfer learning. To check the effectiveness of transfer learning, a small-scale smoke extraction training set was made, and the smoke extraction performance was compared before and after applying transfer learning using a public cloud dataset. As a result, not only the performance in the visible light wavelength band was enhanced but also in the near infrared (NIR) and short-wave infrared (SWIR). Through the results of this study, it is expected that the lack of datasets, which is a critical limit for using deep learning on smoke extraction, can be solved, and therefore, through the advancement of smoke extraction technology, it will be possible to present an advantage in monitoring wildfires.