• Aerospace Engineering and Technology
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• v.8 no.1
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• pp.132-137
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• 2009

This study has regard to classification by using multi-temporal SAR data. Multi-temporal JERS-1 SAR images are used for extract the land cover information and possibility. So far, land cover information extracted by high resolution aerial photo, satellite images, and field survey. This study developed on multi-temporal land cover status monitoring and coherence information mapping can be processing by L band SAR image. From July, 1997 to October, 1998 JERS SAR images (9 scenes) coherence values are analyzed and then extracted land cover information factors, so on. This technique which forms the basis of what is called SAR Interferometry or InSAR for short has also been employed in spaceborne systems. In such systems the separation of the antennas, called the baseline is obtained by utilizing a single antenna in a repeat pass.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.253-259
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• 2004

The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat + ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by National Geographic Information based on aerial photograph and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The classification result by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

• Journal of Forest and Environmental Science
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• v.40 no.3
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• pp.250-258
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• 2024

Forests are crucial for ecosystem stability, societal advancement, and subsistence; however, environmental changes since the 1970s, including shifting agriculture, deforestation, urbanization, increasing human population, and drought, have significantly impacted the region. The purpose of this study was to investigate the status of temporal forest cover changes in the Benchi-Sheko zone in Southwestern Ethiopia. Two types of data were collected: spatial data from satellite images of 1973, 1988, 2003, and 2017, and GPS point data. GIS software, ERDAS version 2015 software, and a handheld GPS were used for data analysis. The data of both GIS from image classification and ERDAS quantification revealed that forest cover decreased from 46,306.17 (92.67%) hectares in 1973 to 27,937.89 (55.91%) hectares in 2017; therefore 18,368.28 hectares (36.76%) decrease in forest cover was detected in the last 44 years by an average annual change of 417.46 hectares and. Based on this, it is recommended that partners working on forest conservation in the locality should enhance local people's awareness to protect forests and forest products in their day-to-day activities.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.409-420
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• 2012

Since the Industrial Revolution, CO2 levels have been increasing with climate change. In this study, Analyze time-series changes in snow cover quantitatively and predict the vanishing point of snow cover statistically using remote sensing. The study area is Mt. Kilimanjaro, Tanzania. 23 image data of Landsat-5 TM and Landsat-7 ETM+, spanning the 27 years from June 1984 to July 2011, were acquired. For this study, first, atmospheric correction was performed on each image using the COST atmospheric correction model. Second, the snow cover area was extracted using the NDSI (Normalized Difference Snow Index) algorithm. Third, the minimum height of snow cover was determined using SRTM DEM. Finally, the vanishing point of snow cover was predicted using the trend line of a linear function. Analysis was divided using a total of 23 images and 17 images during the dry season. Results show that snow cover area decreased by approximately $6.47km^2$ from $9.01km^2$ to $2.54km^2$, equivalent to a 73% reduction. The minimum height of snow cover increased by approximately 290 m, from 4,603 m to 4,893 m. Using the trend line result shows that the snow cover area decreased by approximately $0.342km^2$ in the dry season and $0.421km^2$ overall each year. In contrast, the annual increase in the minimum height of snow cover was approximately 9.848 m in the dry season and 11.251 m overall. Based on this analysis of vanishing point, there will be no snow cover 2020 at 95% confidence interval. This study can be used to monitor global climate change by providing the change in snow cover area and reference data when studying this area or similar areas in future research.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1C
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• pp.24-31
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• 2003

In this paper, a new opto-digital multiple information hiding and real-time extracting system is implemented. That is, multiple information is hidden in a cover image by using the stego keys which are generated by combined use of random sequence(RS) and Hadamard matrix(HM) and these hidden information is extracted in real-time by using a new optical correlator-based extraction system. In the experiment, 3 kinds of information, English alphabet of "N", "R", "L" having 512$\times$512 pixels, are formulated 8$\times$8 blocks and each of these information is multiplied with the corresponding stego keys having 64$\times$64 pixels one by one. And then, by adding these modulated data to a cover image of "Lena"having 512$\times$512 pixels, a stego image is finally generated. In this paper, as an extraction system, a new optical nonlinear joint transform correlator(NJTC) is introduced to extract the hidden data from a stego image in real-time, in which optical correlation between the stego image and each of the stego keys is performed and from these correlation outputs the hidden data can be asily exacted in real-time. Especially, it is found that the SNRs of the correlation outputs in the proposed optical NJTC-based extraction system has been improved to 7㏈ on average by comparison with those of the conventional JTC system under the condition of having a nonlinear parameter less than k=0.4. This good experimental results might suggest a possibility of implementation of an opto-digital multiple information hiding and real-time extracting system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1938-1962
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• 2015

Security of information during transmission is a major issue in this modern era. All of the communicating bodies want confidentiality, integrity, and authenticity of their secret information. Researchers have presented various schemes to cope with these Internet security issues. In this context, both steganography and cryptography can be used effectively. However, major limitation in the existing steganographic methods is the low-quality output stego images, which consequently results in the lack of security. To cope with these issues, we present an efficient method for RGB images based on gray level modification (GLM) and multi-level encryption (MLE). The secret key and secret data is encrypted using MLE algorithm before mapping it to the grey-levels of the cover image. Then, a transposition function is applied on cover image prior to data hiding. The usage of transpose, secret key, MLE, and GLM adds four different levels of security to the proposed algorithm, making it very difficult for a malicious user to extract the original secret information. The proposed method is evaluated both quantitatively and qualitatively. The experimental results, compared with several state-of-the-art algorithms, show that the proposed algorithm not only enhances the quality of stego images but also provides multiple levels of security, which can significantly misguide image steganalysis and makes the attack on this algorithm more challenging.

• Journal of Korea Water Resources Association
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• v.38 no.11
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• pp.927-935
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• 2005

Soil erosion by rainfall is important factor for basin management because it reduces reservoir capacity and breaks out the contamination of water caused by turbid water. Recently, soil erosion study with GIS is in progress but does not consider soil erosion source area. This study calculated soil erosion amount using GIS-based soil erosion model in Imha basin and examined soil erosion source area using SPOT 5 High-resolution satellite image and land cover map. As a result of analysis, dry field showed high-density soil erosion area and we could easily investigate source area using satellite image. Also we could examine the suitability of soil erosion area by applying field survey method in common areas such as dry field and orchard area those are difficult to confirm soil erosion source area using satellite image.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.1-11
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• 2021

Detection of surface water features including river, wetland, reservoir from the satellite imagery can be utilized for sustainable management and survey of water resources. This research compared the water indices derived from the multispectral bands and the machine learning technique for detecting the surface water features from he Landsat-8 satellite image acquired in Daegu through the following steps. First, the NDWI(Normalized Difference Water Index) image and the MNDWI(Modified Normalized Difference Water Index) image were separately generated using the multispectral bands of the given Landsat-8 satellite image, and the two binary images were generated from these NDWI and MNDWI images, respectively. Then SVM(Support Vector Machine), the widely used machine learning techniques, were employed to generate the land cover image and the binary image was also generated from the generated land cover image. Finally the error matrices were used for measuring the accuracy of the three binary images for detecting the surface water features. The statistical results showed that the binary image generated from the MNDWI image(84%) had the relatively low accuracy than the binary image generated from the NDWI image(94%) and generated by SVM(96%). And some misclassification errors occurred in all three binary images where the land features were misclassified as the surface water features because of the shadow effects.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.