• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.564-564
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• 2004

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.33-38
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• 2013

Smart phones that are equipped with various types of sensors can monitor human beings, and their social activities and environments. Smart phone sensing systems are inevitable to lose sensing data at a certain region. It is more severe effect on opportunistic sensing because this sensing scheme is designed to read values from sensors when the state of numberous users meets pre-defined conditions. In this paper, we suggested an estimation model of missing data considering features of smart phone sensing to solve lower quality of collected data. This proposed model does not only reflect a temporal and spatial correlation, but also give high priority to participants who provide high quality data to improve the accuracy of estimated values. The experimental results show that our scheme is more accurate than previous scheme.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.178-181
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• 2003

The temporal GIS data modeling methods are discussed in this paper. At first, two conceptual models of spatio-temporal data are introduced, and then some typical STDMs based on these two models are summed up and compared. After that, the spatio-temporal changes are analyzed thoroughly, and then how to model spatio -temporal data from different aspects is discussed. At last, several issues that need further research are pointed out.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.2
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• pp.79-86
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• 2016

In wireless sensor networks, increasing the sensing rate of each node to improve the data accuracy usually incurs a decrease of network lifetime. In this study, an energy-adaptive data compression scheme is proposed to efficiently control the sensing rate in an energy-harvesting wireless sensor network (WSN). In the proposed scheme, by utilizing the surplus energy effectively for the data compression, each node can increase the sensing rate without any rise of blackout time. Simulation result verifies that the proposed scheme gathers more amount of sensory data per unit time with lower number of blackout nodes than the other compression schemes for WSN.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.310-313
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• 2007

In this study, a new technique for atmospheric transmittance estimated from ALOS PRISM data is developed. It is based on satellite's observing radiances of different view angles and assumes that the cause of difference in radiances is the different view angles. The ALOS PRISM has three independent optical systems for viewing forward and backward and producing a stereoscopic image along the satellite's track. This stereo pair data can be used to estimate the transmittance according to the radiative transfer theory. This derived transmittance will be validated by the AERONET data and compared with the MODTRAN4 simulation results. Results show that the higher the land cover albedo, the better the derived transmittance compared to the AERONET data. Besides, this technique also shows the transmittance retrieval will be underestimated for the low land cover albedo.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.673-678
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• 2002

A satellite remote sensing is useful for vegetation monitoring. But it has some problem. One of these, it is difficult to find a difference of vegetation surface roughness using satellite remote sensing. Each vegetation type has unique surface roughness, for example needle leaves forest, broad leaves forest and grassland. Difference of vegetation surface roughness can be detected by satellite multiangular observation. In this study, objective is to propose index of vegetation surface roughness using BRF property. General vegetation indices are calculated from nadir data of satellite data. A proposed index is calculated from two different observation zenith angle data. Two different zenith data can provide BRF (Bi-directional Reflectance Factor) property of satellite observation data. A proposed index was able to detect different value on where NDVI shows similar high value areas of rice field and forest. This index is useful for vegetation monitoring.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.129-131
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• 2003

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. The topographic and geologic data and satellite image were collected, processed and constructed into a spatial database using GIS and image processing. The used factors that influence landslide occurrence are topographic slope, topographic aspect topographic curv ature and distance from drainage from topographic database, geology and distance from lineament from the geologic database, land use from TM satellite image and vegetation index value from SPOT satellite image. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by probability - likelihood ratio - method. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the hazard map and the existing data on landslide location.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.6
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• pp.141-146
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• 2008

Recently, it is studying to provide to users through internet in the SensorWeb of OGC(Open Geospatial Consortium) saving and maintaining data and image information gathered from sensor network. It is necessary to study about data convergence as binding audio and video for delivering the sensing data and image information to users with real-time system. In this article, it suggests how to convergence sensing data and moving picture collected from the sensor network using index. This program indicates both of them that collected sensing data and information identified of moving picture in the integration index and based on this program provides sensing data moving picture at the same time referencing integration index, if the user asks. To verify suggested method designing real-time multimedia service structure using sensor network and image installation and implementing Ubiquitous realtime multimedia system integrating moving picture and sensing data based on index. As a result of this program, it is confirmed providing real-time multimedia service to request information of application service using integration index collected image and sensing data from wireless sensor network and image installation suggested data convergence method.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.337-340
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• 2007

Many researches conducted to investigate the relationship between surface emissivity and surface temperature in the past two decades and pointed out that the emissivity play a key role in applying remote sensing data to retrieve surface temperature. The task of surface temperature estimation is so important in many research fields, such as earth energy budgets, evapotranspiration, drought, global change and heat island effect. Therefore, it is indispensable to develop an effective and accurate technique to estimate the emissivity for accurate surface temperature estimations. This study developed an improved emissivity estimation technique for the use of surface temperature retrievals with MODIS data. The result of applying this improved technique using Band 31 of MODIS shows that the accuracy of estimated surface temperatures will be improved. This study also uses MODIS data observed in 2005 to establish the relationship between the surface emissivity correction factor and NDVI. Through the use of these correction factors, the land surface temperature can be retrieved more accurate with MODIS data.

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.315-327
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• 2004

Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.