• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.471-476
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• 2010

This study aimed at monitoring, mapping, and assessing the land degradation in the desert area. In this research, the Landsat TM and ETM+ imageries to assess the extent of land degradation for study area during the period from 1991 to 2007. Were used to study supervized, unsupervized classfication and NDVI land cover changes in the desert area in Mongolia. The classified map consists of five classes of water, vegetation, slight desertification, middle desertification and sever desertification. It shows that for determination classfication methods and NDVI, desertification map of the study area are prepared. The result showed accounting for a clear deterioration in vegetative cover, an increase of sever desertification and a decrease in middle desertification and slight desertification respectively of the total study area.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.1-14
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• 2017

A land cover map provides basic information to help understand the current state of a region, but its utilization in the ecological research field has deteriorated due to limited temporal and spatial resolutions. The purpose of this study was to investigate the possibility of using a land cover map with data based on high resolution images acquired by UAV. Using the UAV, 10.5 cm orthoimages were obtained from the $2.5km^2$ study area, and land cover maps were obtained from object-based and pixel-based classification for comparison and analysis. From accuracy verification, classification accuracy was shown to be high, with a Kappa of 0.77 for the pixel-based classification and a Kappa of 0.82 for the object-based classification. The overall area ratios were similar, and good classification results were found in grasslands and wetlands. The optimal image segmentation weights for object-based classification were Scale=150, Shape=0.5, Compactness=0.5, and Color=1. Scale was the most influential factor in the weight selection process. Compared with the pixel-based classification, the object-based classification provides results that are easy to read because there is a clear boundary between objects. Compared with the land cover map from the Ministry of Environment (subdivision), it was effective for natural areas (forests, grasslands, wetlands, etc.) but not developed areas (roads, buildings, etc.). The application of an object-based classification method for land cover using UAV images can contribute to the field of ecological research with its advantages of rapidly updated data, good accuracy, and economical efficiency.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• Journal of Digital Contents Society
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• v.13 no.4
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• pp.531-538
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• 2012

Visual cryptography is a scheme that recovers secret image through human vision by overlapping distributed share images without cryptographic operations. Distribution methods are still being developed for improving quality of shared images keeping size of images invariant and enhancing robustness against resize of images. Since visual cryptography only uses shared images, this fact is exploited to attack. From this fact, a scheme safe for sharing distributed images is needed. In this paper, a new visual cryptographic scheme using cover image is proposed. This scheme reduces the chance of detection against steganalysis and increases security. In addition, this paper shows that the proposed scheme can completely decrypt secret image without creating noise.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.472-479
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• 1999

In this paper, we propose a land cover pattern classifier for remote sensing image by using neuro-fuzzy algorithm. The proposed pattem classifier has a 3-layer feed-forward architecture that is derived from generic fuzzy perceptrons, and the weights are con~posed of h u y sets. We also implement a neuro-fuzzy pattern classification system in the Visual C++ environment. To measure the performance of this, we compare it with the conventional neural networks with back-propagation learning and the Maximum-likelihood algorithms. We classified the remote sensing image into the eight classes covered the majority of land cover feature, selected the same training sites. Experimental results show that the proposed classifier performs well especially in the mixed composition area having many classes rather than the conventional systems.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.109-114
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• 2021

As various users increase in a network environment, it is difficult to protect sensitive and confidential information transmitted and received from attackers. Concealing bitwise secret data in an image using the LSB technique can be very vulnerable to attack. To solve this problem, a hybrid method that combines encryption and information hiding is used. Therefore, an effective method for users to securely protect secret messages and implement secret communication is required. A new approach is needed to improve security and imperceptibility to ensure image quality. In this paper, I propose an LSB steganography technique that hides Hangul messages in a cover image based on MSB and LSB. At this time, after separating Hangul into chosung, jungsung and jongsung, the secret message is applied with Exclusive-OR or Exclusive-NOR operation depending on the selected MSB. In addition, the calculated secret data is hidden in the LSB n bits of the cover image converted by Fibonacci technique. PSNR was used to confirm the effectiveness of the applied results. It was confirmed 41.517(dB) which is suitable as an acceptable result.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.143-151
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• 2021

The enormous prevalence of transferring official confidential digital documents via the Internet shows the urgent need to deliver confidential messages to the recipient without letting any unauthorized person to know contents of the secret messages or detect there existence . Several Steganography techniques such as the least significant Bit (LSB), Secure Cover Selection (SCS), Discrete Cosine Transform (DCT) and Palette Based (PB) were applied to prevent any intruder from analyzing and getting the secret transferred message. The utilized steganography methods should defiance the challenges of Steganalysis techniques in term of analysis and detection. This paper presents a novel and robust framework for color image steganography that combines Linear Congruential Generator (LCG), simulated annealing (SA), Cesar cryptography and LSB substitution method in one system in order to reduce the objection of Steganalysis and deliver data securely to their destination. SA with the support of LCG finds out the optimal minimum sniffing path inside a cover color image (RGB) then the confidential message will be encrypt and embedded within the RGB image path as a host medium by using Cesar and LSB procedures. Embedding and extraction processes of secret message require a common knowledge between sender and receiver; that knowledge are represented by SA initialization parameters, LCG seed, Cesar key agreement and secret message length. Steganalysis intruder will not understand or detect the secret message inside the host image without the correct knowledge about the manipulation process. The constructed system satisfies the main requirements of image steganography in term of robustness against confidential message extraction, high quality visual appearance, little mean square error (MSE) and high peak signal noise ratio (PSNR).

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.159-162
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• 1998

Land cover and land use classification on a huge scale, e.g. national or continental scale, has become more and more important because environmental researches need land cover: And land use data on such scales. We developed a crop field extraction method, which is one of the steps in our land cover classification system for a huge area. Firstly, a crop field model is defined to characterize "crop field" in terms of NDVI value and textual information Textual information is represented by the density of straight lines which are extracted by wavelet transform. Secondly, candidates of NDVI threshold value are determined by "scale-space filtering" method. The most appropriate threshold value among the candidates is determined by evaluating the line density of the area extracted by the threshold value. Finally, the crop field is extracted by applying level slicing to Landsat TM image with the threshold value determined above. The experiment demonstrates that the extracted area by this method coincides very well with the one extracted by visual interpretation.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1271-1275
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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 Ministry of Construction & Transportation based on NGIS (National Geographic Information System) 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 results by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.