• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.114-117
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• 2009

A new video inpainting algorithm is proposed for removing unwanted objects or error of sources from video data. In the first step, the block bundle is defined by the motion information of the video data to keep the temporal consistency. Next, the block bundles are arranged in the 3-dimensional graph that is constructed by the spatial and temporal correlation. Finally, we pose the inpainting problem in the form of a discrete global optimization and minimize the objective function to find the best temporal bundles for the grid points. Extensive simulation results demonstrate that the proposed algorithm yields visually pleasing video inpainting results even in a dynamic scene.

• Korean Journal of Remote Sensing
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• v.20 no.1
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• pp.13-21
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• 2004

The usefulness of the multi-temporal satellite image to monitoring the vegetation recovery process after forest fire was tested. Using multi-temporal Landsat TM and ETM＋data, NDVI and NBR changes over times were analyzed. Both NDVI and NBR values were rapidly decreased after the fire and gradually increased for all forest type and damage class. However, NBR curve showed much clearer tendency of vegetation recovery than NDVI. Both indices yielded the lowest values in severely damaged red pine forest. The results show the vegetation recovery process after forest fire can detect and monitor using multi-temporal Landsat image. NBR was proved to be useful to examine the recovering and development process of the vegetation after fire. In the not damaged forest, however the NDVI shows more potential capability to discriminate the forest types than NBR..

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.205-214
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• 2015

In this study, a novel land-cover classification framework for multi-temporal SAR data is presented that can combine multiple features extracted through data transforms and multiple classifiers. At first, data transforms using principle component analysis (PCA) and 3D wavelet transform are applied to multi-temporal SAR dataset for extracting new features which were different from original dataset. Then, three different classifiers including maximum likelihood classifier (MLC), neural network (NN) and support vector machine (SVM) are applied to three different dataset including data transform based features and original backscattering coefficients, and as a result, the diverse preliminary classification results are generated. These results are combined via a majority voting rule to generate a final classification result. From an experiment with a multi-temporal ENVISAT ASAR dataset, every preliminary classification result showed very different classification accuracy according to the used feature and classifier. The final classification result combining nine preliminary classification results showed the best classification accuracy because each preliminary classification result provided complementary information on land-covers. The improvement of classification accuracy in this study was mainly attributed to the diversity from combining not only different features based on data transforms, but also different classifiers. Therefore, the land-cover classification framework presented in this study would be effectively applied to the classification of multi-temporal SAR data and also be extended to multi-sensor remote sensing data fusion.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.36-44
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• 2018

VR(Virtual Reality) and AR(Augmented Reality) devices are becoming more common, and the need for proper contents presentation techniques in such environments has been growing ever since the popularization of the devices. One of the contents is the spatio-temporal data, which has become more prominent since it could be both generated and consumed by a large number of ordinary users. In this work, the researcher analyzed the characteristics of spatio-temporal data as a source for visualization in VR and AR environment, and categorized prior visualization methods for such data, which were devised for traditional monitors. The researcher also reviewed the hardware specification of state-of-the-art devices, and examined the possibility of adopting the previous visualization approaches. This work is expected to contribute in designing spatio-temporal visualization for VR and AR environment by utilizing their unique characteristics.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1529-1541
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• 2006

Moving objects have been widely employed in traffic and logistic applications. Spatio-temporal aggregations mainly describe the moving object's behavior in the spatial data warehouse. The previous works usually express the object moving in some certain region, but ignore the object often moving along as the trajectory. Other researches focus on aggregation and comparison of trajectories. They divide the spatial region into units which records how many times the trajectories passed in the unit time. It not only makes the storage space quite ineffective, but also can not maintain spatial data property. In this paper, a spatio-temporal aggregation index structure for moving object trajectory in constrained network is proposed. An extended B-tree node contains the information of timestamp and the aggregation values of trajectories with two directions. The network is divided into segments and then the spatial index structure is constructed. There are the leaf node and the non leaf node. The leaf node contains the aggregation values of moving object's trajectory and the pointer to the extended B-tree. And the non leaf node contains the MBR(Minimum Bounding Rectangle), MSAV(Max Segment Aggregation Value) and its segment ID. The proposed technique overcomes previous problems efficiently and makes it practicable finding moving object trajectory in the time interval. It improves the shortcoming of R-tree, and makes some improvement to the spatio-temporal data in query processing and storage.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.11-20
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• 2012

Stream data shows a sequence of values changing continuously over time. Due to the nature of stream data, its trend is continuously changing according to various time intervals. Therefore the prediction of stream data must be carried out simultaneously with respect to multiple intervals, i.e. Continuous Multiple Prediction(CMP). In this paper, we propose a Continuous Integrated Hierarchical Temporal Memory (CIHTM) network for CMP based on the Hierarchical Temporal Memory (HTM) model which is a neocortex leraning algorithm. To develop the CIHTM network, we created three kinds of new modules: Shift Vector Senor, Spatio-Temporal Classifier and Multiple Integrator. And also we developed learning and inferencing algorithm of CIHTM network.

• Journal of Positioning, Navigation, and Timing
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• v.2 no.2
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• pp.109-114
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• 2013

The vulnerability of GPS to interference signals was reported in the early 2000s, and an eLORAN system has been suggested as a backup navigation system for replacing the existing GPS. Thus, relevant studies have been carried out in the United States, Europe, Korea, etc., and especially, in Korea, the research and development is being conducted for the FOC of the eLORAN system by 2018. The required performance of the eLORAN system is to meet the HEA performance, and to achieve this, it is essential to perform ASF correction based on a dLORAN system. ASF can be divided into temporal ASF, nominal ASF, and spatial ASF. Spatial ASF is the variation due to spatial characteristics, and is stored in an eLORAN receiver in the form of a premeasured map. Temporal ASF is the variations due to temporal characteristics, and are transmitted from a dLORAN site to a receiver via LDC. Unlike nominal ASF that is obtained by long-term measurement (over 1 year), temporal ASF changes in a short period of time, and ideally, real-time correction needs to be performed. However, it is difficult to perform real-time correction due to the limit of the transmission rate of the LDC for transmitting correction values. In this paper, to determine temporal ASF correction frequency that shows satisfactory performance within the range of the limit of data transmission rates, relative variations of temporal ASF in summer and winter were measured, and the stability of correction values was analyzed using the average of temporal ASF for a certain period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1B
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• pp.13-20
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• 2011

The spatio-temporal pattern in precipitation is a significant element in defining characteristics of precipitation. In this study, a new scheme on regionalization utilizing temporal information was introduced on the basis of existing approaches that is mainly based on simple moments of data and geographical information. Given the identified spatio-temporal pattern, this study was extended to characterize regional pattern of annual maximum rainfall over Korea. We have used circular statistics to characterize the temporal distribution on the precipitation, and the circular statistics allow us to effectively assess changes in timing of the extreme rainfall in detail. In this study, a modified K-means method was incorporated with derived temporal characteristics of extreme rainfall in order to better characterize hydrologic pattern for regional frequency analysis. The extreme rainfall was reasonably separated into five categories that considered most attributes in both quantitative and temporal changes in extremes. The results showed that the proposed approach is a promising approach for regionalization in term of physical understanding of extreme rainfall.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.574-575
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• 2018

The generation of redundant data according to the spatial-temporal correlation in a wireless sensor network that reduces the network lifetime by consuming unnecessary energy. In this paper, data collection experiment through the particulate matter sensor is carried out to confirm the spatial-temporal data redundancy and we propose permitted limit setting method for data transmission to solve this problem. In the proposed method, the data transmission permitted limit is set by using the integrated average value in the cluster. The set permitted limit reduces the redundant data of the member node and it is shows that redundant data reduction is possible even in a variable environment of collected data by resetting the permitted limit in the cluster head.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.381-392
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• 2003

Multi-temporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land-cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. A harmonic model that can represent seasonal variability is characterized by four components: mean level, frequency, phase and amplitude. The trigonometric components of the harmonic function inherently contain temporal information about changes in land-cover characteristics. Using the estimates which are obtained from sequential images through spectral analysis, seasonal periodicity can be incorporates into multi-temporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 ～ 2000 using a dynamic technique. Land-cover types were then classified both with the estimated harmonic components using an unsupervised classification approach based on a hierarchical clustering algorithm. The results of the classification using the harmonic components show that the new approach is potentially very effective for identifying land-cover types by the analysis of its multi-temporal behavior.