• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.361-364
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• 2008

It is well known that combining spatial and spectral information can improve land use classification from satellite imagery. High spatial resolution classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, extracting the spatial information is one of the most important steps in high resolution satellite image classification. In this paper, we propose a new spatial feature extraction method. The extracted features are integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a Support Vector Machines classifier. In order to evaluate the proposed feature extraction method, we applied our approach to KOMPSAT-2 data and compared the result with the other methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.64-72
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• 1996

In this paper, we propose predictive motion estimatin algorithm which can predict motion without additional side information considering spatio-tempral correlatio of motion vector. This method performs motion prediction of current block using correlation of the motion vector for two spatially adjacent blocks and a temporally adjacent block. Form predicted motion, the position of searhc area is determined. Then in this searhc area, we estimate motion vector of current block using block matching algoirthm. Considering spatial an temporal correlation of motion vector, the proposed method can predict motion precisely much more. Especially when the motion of objects is rapid, this method can estimate motion more precisely without reducing block size or increasing search area. Futhrmore, the proposed method has computation time the same as conventional block matching algorithm. And as it predicts motion from adjacent blocks, it does not require additional side information for adjacent block. Computer simulation results show that motion estimation of proposed method is more precise than that of conventioanl method.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.45-53
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• 2005

Nowadays, using the mobile communication devices, such as a mobile phone, PDA, telematics device, and so forth, are increasing. The large parts of the services with these mobile devices are the position tracking and the route planning. For offering these services, it is increasing the use of the spatial data on the mobile environment. Although the storage of mobile device expands more than before, it still lacks the necessary storage on the spatial data. In this paper, lossy compression technique on the spatial data is suggested, and then it is analyzed the compression ratio and the amount of loss data by the test. Suggested compression technique on the spatial data at this paper is applied to the real-data, and others methods, suggested at the previous studies, is applied to same data. According as the results from both are compared and analyzed, compression technique suggested at this study shows better performance when the compression result is demanded the high position accuracy.

• Spatial Information Research
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• v.10 no.2
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• pp.233-246
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• 2002

The purpose of this paper is to quantitatively measure the effect of location in evaluating the land value through the implementation of GIS coupled with spatial statistical analysis. We assumed that the hedonic price model, which was commonly used in modelling the land value, could not explain the spatial factor effectively. In order to add the spatial factor, the analysis of the spatial autocorrelation was used. The present project used 54 standard land price samples from 1421 parcel land values and applied Kriging to predict stochastically the unsampled values on the basis of spatial autocorrelation between location of vector data. This study confirms that the spatial variogram analysis has an advantage of predicting spatial dependence process and revealing the positive premium and the negative penality on location factor objectively.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.651-656
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• 2001

Histogram intersection method, that counts the occurrence of color pixels, is one of the easy and simple color image retrieval methods. The method has an appropriate global property but does not contain the knowledge of shape for images. The absence of spatial information makes it difficult to discriminate images of the similar histogram. The application of one-dimensional projection to each image enables to obtain shape or spatial information of image. But in this case there is another problem having different length of the projection vector according to the size of each image. Thus this paper proposes a method that uses relative distances between peaks and their maximum value in the projection vector. In order to verify retrieval performance, the experimental results between the histogram intersection method, the projection only method, and the proposed one are compared and analyzed.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.393-400
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• 2005

Due to the development of the network and Internet, it is easy to copy and spread digital data. These data has the advantage of being able to be copy without loss. However, this has generated a problem over copyright. The problem occurred in GIS, too. Although GIS data acquisition is the major cost there is insufficient effort made to protect copyright. For this reason watermarking could be a good method to guarantee owner's copyright. This paper will explain watermarking, and show an overview of watermarking studies connecting image and vector data.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.329-339
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• 2003

In this study, an approach of image fusion in decision level has been proposed for unsupervised image classification using the images acquired from multiple sensors with different characteristics. The proposed method applies separately for each sensor the unsupervised image classification scheme based on spatial region growing segmentation, which makes use of hierarchical clustering, and computes iteratively the maximum likelihood estimates of fuzzy class vectors for the segmented regions by EM(expected maximization) algorithm. The fuzzy class vector is considered as an indicator vector whose elements represent the probabilities that the region belongs to the classes existed. Then, it combines the classification results of each sensor using the fuzzy class vectors. This approach does not require such a high precision in spatial coregistration between the images of different sensors as the image fusion scheme of pixel level does. In this study, the proposed method has been applied to multispectral SPOT and AIRSAR data observed over north-eastern area of Jeollabuk-do, and the experimental results show that it provides more correct information for the classification than the scheme using an augmented vector technique, which is the most conventional approach of image fusion in pixel level.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.57-60
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• 2000

This paper presents a new adaptive fast motion estimation algorithm along with its architecture. The conventional algorithm such as full - search algorithm, three step algorithm have some disadvantages which are related to the amount of computation, the quality of image and the implementation of hardware, the proposed algorithm uses spatial correlation and a slope of motion vector in order to reduce the amount of computation and preserve good image quality, The proposed algorithm is better than the conventional Block Matching Algorithm(BMA) with regard to the amount of computation and image quality. Also, we propose an efficient at chitecture to implement the proposed algorithm. It is suitable for real time processing application.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.91-102
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• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.9-12
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• 2002

In this Paper, we propose an efficient de-interlacing algorithm using temporal and spatial domain information. In the proposed scheme, motion estimation is performed same parity fields, i.e., if current field is even field, reference fields are previous even field and forward even field. And then motion vector refinement is performed to improve the accuracy of motion vectors. In the interpolating step, we use median filter to reduce the interpolation error caused by incorrect motion vector. Simulations conducted for various video sequences have shown the efficiency of the proposed interpolator with significant improvement over previous methods in terms of both PSNR and perceived image quality.