• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.653-663
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• 2012

Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called "super-resolution mapping" to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.83-91
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• 2019

In this paper, we have effectively used the surface characteristics and local similarity existing in image, solved the problem that there is no CZP(Closest Zero point) that occurs in a very few images to hide secrete data into cover image by using histogram shift. By applying the proposed technique, it is possible to hide secrete data invisibly into the cover image, extract secrete data from the stego-image with no data loss, and completely restore the original cover image. It is impossible to know whether the secrete data is hidden in the stego-image because the stego-image constructed by applying the proposed technique has a good visual quality that can not distinguish the difference from the cover image. The proposed method is able to hide secrete data at various levels compared to conventional APD(Adjacent Pixel Difference) technique, and hide secrete data up to 25.1% more than APD in cover image.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1030-1032
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• 2003

This study a imed to analyze the effect of combined optical and radar image for the land cover classification in coastal region. The study area, Gyeonggi Bay area has one of the largest tidal ranges and has frequent land cover changes due to the several reclamations and rather intensive land uses. Ten land cover types were classified using several datasets of combining Landsat ETM+ and RADARSAT imagery. The synergic effects of the merged datasets were analyzed by both visual interpretation and an ordinary supervised classification. The merged optical and SAR datasets provided better discrimination among the land cover classes in the coastal area. The overall classification accuracy of merged datasets was improved to 86.5% as compared to 78% accuracy of using ETM+ only.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.3
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• pp.289-297
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• 2006

The Land Cover/Land Use Map have been constructed from 1998, which has hierarchical structure according to land cover/land use system. Level 1 classification Map have done using Landsat satellite image over whole Korean peninsula. Level II classification Map have been digitized using IRS-1C, 1D, KOMPSAT and SPOT5 satellite images resolution-merged with low resolution color images. Level II Land Cover/Land Use Map construction by digitizing method, however, is consuming enormous expense for satellite image acquisition, image process and Land Cover/Land Use Map construction. In this paper, the possibility of constructing Level II Land Cover/Land Use Map using hyperspectral satellite image of EO-1 Hyperion, which is studied a lot recently, is studied. The comparison of classifications using Hyperion satellite image offering more spectral information and Landsat-7 ETM+ image is performed to evaluate the availability of Hyperion satellite image. Also, the algorithm of the optimal band selection is presented for effective application of hyperspectral satellite image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5095-5111
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• 2019

A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.617-625
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• 2017

In this paper, an effective reversible data embedding algorithm was proposed to embed secrete data into image. In the proposed algorithm, prediction image is generated by accurately predicting pixel values using local similarity existing in image, difference sequence is generated using the generated prediction image and original cover image, and then histogram shift technique is applied to create a stego-image with secrete data hidden. Applying the proposed algorithm, secrete data can be extracted from the stego-image and the original cover image can be restored without loss. Experimental results show that it is possible to embed more secrete data into cover image than APD algorithm by applying the proposed algorithm.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.205-213
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• 2018

Hyperspectral image is more increasing spectral resolution that Multi-spectral image. Because of that, each pixel of the hyperspectral image includes much more information and it is considered the most appropriate technic for land cover classification. but recent research of hyperspectral image is stayed land cover classification of general level. therefore we classified land cover of detail level using ED, SAM, SSS method and made Decision Tree from result of that. As a result, the overall accuracy of general level was improved by 1.68% and the overall accuracy of detail level was improved by 5.56%.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.383-387
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• 2007

The improved accuracy of the Land Cover/Land Use Map constructed using Hyperspectal Satellite Image and the possibility of real time classification of Land Use using optimal Band Selective Factor enable the change detection from automatic classification using the existed Land Cover/Land Use Map and the newly acquired Hyperspectral Satellite Image. In this study, the effective analysis techniques for automatic generation of training regions, automatic classification and automatic change detection are proposed to minimize the expert's interpretation for automatic update of the Land Cover/Land Use Map. The proposed algorithms performed successfully the automatic Land Cover/Land Use Map construction, automatic change detection and automatic update on the image which contained the changed region. It would increase applicability in actual services. Also, it would be expected to present the effective methods of constructing national land monitoring system.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.985-997
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• 2003

The objective of this study is to propose Runoff Curve Numbers(CNs) for land cover and treatment classification of satellite image. For this purpose, land cover classifications by using satellite image in addition to the exiting SCS's land cover and treatment classifications studies and land cover classifications suggested by Ministry of Environment are selected to provide CNs depending on the classifications. CNs estimation method is statistical approach that is suggested by Hjelmfelt(1991). Result of this study may contribute to use efficiently for the estimation of CNs in using satellite image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5643-5656
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• 2016

The detection accuracy of steganalysis depends on many factors, including the embedding algorithm, the payload size, the steganalysis feature space and the properties of the cover source. In practice, the cover source mismatch (CSM) problem has been recognized as the single most important factor negatively affecting the performance. To address this problem, we propose a new framework for blind, universal steganalysis which uses traditional steganalyst features. Firstly, cover images with the same statistical properties are searched from a reference image database as aided samples. The test image and its aided samples form a whole test set. Then, by assuming that most of the aided samples are innocent, we conduct outlier detection on the test set to judge the test image as cover or stego. In this way, the framework has removed the need for training. Hence, it does not suffer from cover source mismatch. Because it performs anomaly detection rather than classification, this method is totally unsupervised. The results in our study show that this framework works superior than one-class support vector machine and the outlier detector without considering the image retrieval process.