• Journal of the Korean earth science society
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• v.23 no.1
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• pp.1-14
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• 2002

The land-cover of two regions of South and North Korea included in one Landsat TM scene was investigated by comparing different seasons and different band data over the multiple land-cover types. The relationships between the intensities of two bands in the 2-D plot are mainly linear in band2 versus band1 and band3 versus band1, polygonal sporadic in band5 versus band1 and band7 versus band1, and almost tri-polarized in band4 versus band3. The 2-D plot of band4/band3 shows the best capability to discriminate different main land-cover such as water, vegetation and dry soil. Some discriminations are not clear between city and dry field, or mountain and plain field in the scene of September. The digital number data of band4 from vegetated zones show stronger reflectance in September rather than April, while other band values tend to be lager in April than in September over each land-cover. NDVI presents high value in both regions in September. However the image of Wonsan area in April suggests weak vigor of vegetation in comparison with Cholwon area. Band ratios are very effective in eliminating the influence of the complex topography. The proper pairing of the band ratio improved the discrimination capability of the land-cover; band5/band2 for dry soil, band4/band3 for vegetation and band1/band7 for the water. The RGB combination of the three band ratio pairs showed the best results in the discrimination of the land-cover of Wonsan, Cholwon and even the Demilitarized Zone.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Journal of the Korean association of regional geographers
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• v.5 no.1
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• pp.137-150
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• 1999

Five land use change detection methods were applied to 1994 and 1997 Landsat Thematic Mapper (TM) images of Pook-Gu, Taegu city to determine the land-cover changes between the two dates. The two images were coregistred to UTM coordinates. A post-classification comparison method was the most commonly used quantitative method of change detection. A pre-classification comparison method was more effective method to change detection of land cover than a post-classification comparison method. Two indices were used to assess the accuracies of the studied methods. A image differencing method was found to be most accurate for detecting change verse no change among five land use change detection methods. The difference image of band 2 was found to be most accurate. The overall accuracy and Kappa index agreement of the difference image of band 2 were 0.810 and 0.447.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.256-270
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• 2014

A new morphed steganographic algorithm is proposed in this paper. Image security is a challenging problem these days. Steganography is a method of hiding secret data in cover media. The Least Significant Bit is a standard Steganographic method that has some limitations. The limitations are less capacity to hide data, poor stego image quality, and imperceptibility. The proposed algorithm focuses on these limitations. The morphing concept is being used for image steganography to overcome these limitations. The PSNR and standard deviation are considered as a measure to improve stego image quality and morphed image selection, respectively. The stego keys are generated during the morphed steganographic embedding and extracting process. Stego keys are used to embed and extract the secret image. The experimental results, which are based on hiding capacity and PSNR, are presented in this paper. Our research contributes towards creating an improved steganographic method using image morphing. The experimental result indicates that the proposed algorithm achieves an increase in hiding capacity, stego image quality, and imperceptibility. The experimental results were compared with state of the art steganographic methods.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.433-438
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• 1999

The land use type of Korea is high-density. So, the image classification using coarse resolution satellite image may not provide land cover classification results as good as expected. The purpose of this paper is to compare the result of visual interpretation with that of digital image classification of 20 m resolution SPOT satellite image at Kwangju-eup, Kyunggi-do, Korea. Classes are forest, cultivated field, pasture, water and residential area, which are clearly discriminated in visual interpretation. Maximum likelihood classifier was used for digital image classification. Accuracy assessment was done by comparing each classification result with ground truth data obtained from field checking. The classification result from the visual interpretation presented an total accuracy 9.23 percent higher than that of the digital image classification. This proves the importance of visual interpretation for the area with high density land use like the study site in Korea.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.5
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• pp.77-83
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• 2008

In this study, land cover classification and NDVI evaluation for hazard mitigation precaution are carried out in surrounding areas of Yeongi-gun, Chungcheongnam-do ($132\;km^2$) where a project for multi-functional administrative city is promoted by government. Image acquired from KOMPSAT 2, LANDSAT and ASTER is utilized and comparative evaluation on limitation in classification based on resolution was carried out. The area mainly consists of arable land including mountains, rice fields, ordinary fields, etc thus special attention was paid to the classification of rice fields and ordinary fields. For the classification of image acquired from KOMPSAT 2, segmentation technique for classification of high-resolution image was applied. To evaluate the accuracy of the classification, field investigation was conducted to examine the sample and it was compared with the land usage and classification of land category in land ledger of Korea. Acquired results were made into theme map in shape file format and it would be of great help in decision making of policy for the future-oriented development plan of multi-functional administrative city.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.999-1012
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• 2003

The objective of this study is to test the applicability of CN values suggested using land cover and treatment classification of satellite image. Applicability test is based on the comparison of observed effective rainfall and computed one. The 3 case study areas, where are the upstream of Gyeongan stage station, the upstream of Baekokpo stage station Pyungchang River basin, and the upstream of Koesan Dam, are selected to test the proposed CN values and the hydrologic and meteorologic data, Landsat-7 ETM of 2000, soil map of 1:50,000 are collected for the selected areas. The results show that the computed CN values for three study cases are 71, 63, 66, respectively, and the errors between observed and computed effective rainfall are within about 30%. It can be concluded that the proposed CN values from this study for land cover and treatment classification of satellite image not only provides more accurate results for the computation of effective rainfall, but also suggest the objective CN values and effective rainfall.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.549-559
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• 2021

The Arctic Circle's huge amounts of fossil fuels and mineral resources are being developed and subjected to active construction projects. Global efforts are continuing to actively respond to climate change, but the dependence on fossil fuels remains high. This study reports a preliminary survey conducted in Alberta, Canada, where oil sand resources are actively developed. A land cover map was prepared using satellite imagery to reduce the cost and time of surveying a wide area. Results likely useful to resource development projects such as ground surface temperature and snow cover distribution were derived by using the obtained image classification results. It is expected that the results of the present research and analysis will be used to establish strategies for the successful promotion and operation of projects to develop resources in the Arctic.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.353-364
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• 2016

Yangon city is experienced a rapid urban expansion over the last two decades due to accelerate with the socioeconomic development. This research work studied an investigation into the application of the integration of the Remote Sensing (RS) and Geographic Information System (GIS) for observing Land Use and Land Cover (LULC) patterns and evaluate its impact on Land Surface Temperature (LST) of the downtown, suburban 1 and suburban 2 of Yangon city. The main purpose of this paper was to examine and analyze the variation of the spatial distribution property of the LULC of urban spatial information related with the LST and Normalized Difference Vegetation Index (NDVI) using RS and GIS. This paper was observed on image processing of LULC classification, LST and NDVI were extracted from Landsat 8 Operational Land Imager (OLI) image data. Then, LULC pattern was linked with the variation of LST data of the Yangon area for the further connection of the correlation between surface temperature and urban structure. As a result, NDVI values were used to examine the relation between thermal behavior and condition of land cover categories. The spatial distribution of LST has been found mixed pattern and higher LST was located with the scatter pattern, which was related to certain LULC types within downtown, suburban 1 and 2. The result of this paper, LST and NDVI analysis exhibited a strong negative correlation without water bodies for all three portions of Yangon area. The strongest coefficient correlation was found downtown area (-0.8707) and followed suburban 1 (-0.7526) and suburban 2(-0.6923).

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.537-544
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• 2013

Baton Peninsula, where Sejong station is located, mainly covered with snow and vegetation. Because this area is sensitive to climate change, monitoring of surface variation is important to understand climate change on the polar region. Due to the inaccessibility, the remote sensing is useful to continuously monitor the area. The objectives of this research are 1) map classification of land-cover types in the Barton Peninsular around King Sejong station and 2) grasp distribution of vegetation species in classified area. A KOMPSAT-2 multispectral satellite image was used to classify land-cover types and vegetation species. We performed classification with hierarchical procedure using KOMPSAT-2 satellite image and ground reference data, and the result is evaluated for accuracy as well. As the results, vegetation and non-vegetation were clearly classified although species shown lower accuracies within vegetation class.