• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.356-359
• /
• 2006

The high prices and lack of information for satellite images prevent researchers from studying remote sensing and most non-professional people can't have the simple and easy solutions for the manipulation of satellite images. 'Satellite Imagery Information Management Center'(SIMC) project which is promoted by ETRI (Electronics and Telecommunications Research Institute) from 2002 to 2005 in Korea have the purpose to provide the satellite images freely to the public domain and the solutions for the above mentioned problems. Our project have the following five systems; Data Acquisition System, Data Preservation System, Integrated Solution System, Technology Development System, Operation Plan System. Data Acquisition System collects the satellite images such as LANDSAT, IKONOS, etc. Data Preservation System consists of database which registers the diverse satellite images. Integrated Solution System gives the user of public domain for the web service which search, order and transfer the satellite images. Technology Development System has the many processing technologies for the satellite images. Finally, the Operation Plan system has the role to plan the future of our SIMC project. In this paper, we will give the result of SIMC Project for each five systems during the fast four years from 2002 to 2005.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.253-258
• /
• 2014

In smartphone sensing which monitors various social phenomena of the individuals by using embedded sensors, managing metadata is one of the important issue to process large-scale data, improve the data quality, and share collected data. In this paper, we proposed a document clustering scheme for the large-scale metadata management architecture which is designed as a hybrid back-end consisting of a cluster head and member nodes to reduce the server-side overhead. we also verified that the proposed scheme is more efficient than the distance based clustering scheme in terms of the server-side overhead through simulation results.

• Smart Structures and Systems
• /
• v.5 no.2
• /
• pp.119-137
• /
• 2009

Smart sensors densely distributed over structures can use their computational and wireless communication capabilities to provide rich information for structural health monitoring (SHM). Though smart sensor technology has seen substantial advances during recent years, implementation of smart sensors on full-scale structures has been limited. Hardware resources available on smart sensors restrict data acquisition capabilities; intrinsic to these wireless systems are packet loss, data synchronization errors, and relatively slow communication speeds. This paper addresses these issues under the hardware limitation by developing corresponding middleware services. The reliable communication service requires only a few acknowledgement packets to compensate for packet loss. The synchronized sensing service employs a resampling approach leaving the need for strict control of sensing timing. The data aggregation service makes use of application specific knowledge and distributed computing to suppress data transfer requirements. These middleware services are implemented on the Imote2 smart sensor platform, and their efficacy demonstrated experimentally.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.4
• /
• pp.999-1007
• /
• 2018

To implement crowdsensing system in reality, trustiness between service provider server and user is necessary. Service provider server could manipulate the evaluation of sensing data to reduce incentive. Moreover, user could send a fake sensing data to get unjust incentive. In this paper, we adopt private blockchain on crowdsensing system, and thus paid incentives and sent data are unmodifiablely recorded. It makes server and users act as watcher of each others. Through adopting smart contract, our system automates sensing data evaluation and opens to users how it works. Finally, we show the feasibility of proposing system with performance evaluation and comparison with other systems.

• Korean Journal of Remote Sensing
• /
• v.26 no.3
• /
• pp.325-333
• /
• 2010

Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.

• Korean Journal of Remote Sensing
• /
• v.22 no.3
• /
• pp.175-182
• /
• 2006

Because of the phenological variation of vegetation growth in temperate region, it is often difficult to accurately assess the surface conditions of agricultural croplands, grasslands, and disturbed forests by multi-spectral remote sensor data. In particular, the spectral similarity between soil and dry vegetation has been a primary problem to correctly appraise the surface conditions during the non-growing seasons in temperature region. This study analyzes the spectral characteristics of the mixture of dry vegetation and soil. The reflectance spectra were obtained from laboratory spectroradiometer measurement (GER-2600) and from EO-1 Hyperion image data. The reflectance spectra of several samples having different level of dry vegetation fractions show similar pattern from both lab measurement and hyperspectral image. Red-edge near 700nm and shortwave IR near 2,200nm are more sensitive to the fraction of dry vegetation. The use of hyperspectral data would allow us for better separation between bare soils and other surfaces covered by dry vegetation during the leaf-off season.

• Korean Journal of Remote Sensing
• /
• v.22 no.5
• /
• pp.305-311
• /
• 2006

Although there have been several attempts to estimate forest LAI using optical remote sensor data, there are still not enough evidences whether the NDVI is effective to estimate forest LAI, particularly in fully closed canopy situation. In this study, we have conducted a simple correlation analysis between LAI and spectral reflectance at two different settings: 1) laboratory spectral measurements on the multiple-layers of leaf samples and 2) Landsat ETM+ reflectance in the close canopy forest stands with fieldmeasured LAI. In both cases, the correlation coefficients between LAI and spectral reflectance were higher in short-wave infrared (SWIR) and visible wavelength regions. Although the near-IR reflectance showed positive correlations with LAI, the correlations strength is weaker than in SWIR and visible region. The higher correlations were found with the spectral reflectance data measured on the simulated vegetation samples than with the ETM+ reflectance on the actual forests. In addition, there was no significant correlation between the forest.LAI and NDVI, in particular when the LAI values were larger than three. The SWIR reflectance may be important factor to improve the potential of optical remote sensor data to estimate forest LAI in close canopy situation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.10a
• /
• pp.565-568
• /
• 2009

This paper proposes an monitoring system of elder and feeble person's motion activity using an object's motion activity data. The proposed system used wireless 3-axis sensor module, product by Freescale(Wireless Sensing Triple Axis Reference Design Board (ZSTAR)). We distribute sensing data into three classes using Neural Network System SVM. We find performance of proposed system that simulate some case about walk, past walk, fallen. Classify result data and graph of sensing data present succes rate 80%.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.4-4
• /
• 2020

Three wildfires severely damaged local towns and forests in Gangwon-do, South Korea in 2019 April 4-5. Local hydrological regime could be greatly altered by the wildfires, therefore it is important to assess its damage (e.g. area and severity) and also resultant changes in hydrological fluxes. We retrieved the Normalized-Burned Ratio (NBR) index using remote-sensing data (Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m 8-day surface reflectance data), and delineated the damaged-area based on the difference in the NBR (dNBR) before and after the wildfires. We then estimated changes in the annual evapotranspiration (AET) in 2019 using the MODIS evapotranspiration data (500-m 8-day). It was found that the damaged-area of the three wildfires was 29.50 km^2 in total, which take up 1.00-6.19% area of five catchments. It was estimated that the AET would be decreased as 0.05-1.56% over those five catchments, as compared to the pre-fire AET (2004-2018). The impact of the wildfires on the catchment AET was less severe than expected (i.e. up to 1.56%) mostly because two big wildfires were distributed across two catchments respectively (i.e. four catchments for the two wildfires) and the other wildfire was small and not severe. This study highlights the importance of assessing the area and severity of a wildfire when estimating its impact on the local hydrological cycle.

• Journal of the Korean Society of Industry Convergence
• /
• v.26 no.6_3
• /
• pp.1325-1332
• /
• 2023

Recently, much research is being conducted based on point cloud data for the growth of innovations such as construction automation in the transportation field and virtual national space. This data is often measured through remote control in terrain that is difficult for humans to access using devices such as UAVs and UGVs. Drones, one of the UAVs, are mainly used to acquire point cloud data, but photogrammetry using a vision camera, which takes a lot of time to create a point cloud map, is difficult to apply in construction sites where the terrain changes periodically and surveying is difficult. In this paper, we developed a point cloud mapping system by adopting non-repetitive scanning LiDAR and attempted to confirm improvements through field application. For accuracy analysis, a point cloud map was created through a 2 minute 40 second flight and about 30 seconds of software post-processing on a terrain measuring 144.5 × 138.8 m. As a result of comparing the actual measured distance for structures with an average of 4 m, an average error of 4.3 cm was recorded, confirming that the performance was within the error range applicable to the field.