• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.18-21
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• 2007

Digital image watermarking is a technology used in copyrighting of digital images by embedding unremovable informations. In this paper, a pixel-domain look-up-table-based watermarking algorithm is presented. With this methodology, the watermark was embedded in the host image, but we did not observe any distortion at certain specific region of interest. This means the proposed method is preferred in case of satellite images. Then, the image manipulation tool which is called 'StirMark' will be used to perform many kinds of attacks such as rotation, scaling, filtering and compression on the watermarked image. Finally, the effectiveness of a watermarking technique in terms of 'robustness' and 'data integrity' criteria will be measured by calculating PSNR of watermark and watermarked image.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.4
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• pp.37-42
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• 2009

This study extracted Near Infrared (NIR) band using Image Processing Technology (IPT), and calculated Normalized Difference Vegetation Index (NDVI). Aerial photography from Daum portal in combination with high resolution satellite image was employed to improve vegetation sensitivity by extracting NIR band and calculating NDVI with comparison to QuickBird result. The extracted NIR band and NDVI through IPT presented similar distribution pattern. In addition, a regression analysis by land cover character showed high correlation paddy and forest Therefore, this approach could be acceptable to acquire vegetation environment information.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.88-88
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• 2002

Satellite Image archiving system requires large storage and long transmission time. A simple and cheap way of overcoming these limitations is to increase the compression ratio. However this requires a feasibility study for accurate applications. Here, a new still image compression standard is being developed, the JPEG2000. It provides lossless and lossy compression, progressive transmission by pixel accuracy and by resolution, region-of-interest coding, user-defined tiling size, random codestream access and processing etc. In this study, we will briefly introduce the JPEG2000 compression standard which provides a new compression technique based on the wavelet technology and offers better compression ratios, and evaluate the compression ratios of JPEG2000 for satellite image by performing various image quality tests. Also, we will compare brief test result using the commercial remote sensing software.

• Journal of Astronomy and Space Sciences
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• v.23 no.1
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• pp.29-38
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• 2006

This paper presents an Image Motion Compensation (IMC) algorithm for the Korea's Communication, Ocean, and Meteorological Satellite (COMS)-1. An IMC algorithm is a priority component of image registration in Image Navigation and Registration (INR) system to locate and register radiometric image data. Due to various perturbations, a satellite has orbit and attitude errors with respect to a reference motion. These errors cause depointing of the imager aiming direction, and in consequence cause image distortions. To correct the depointing of the imager aiming direction, a compensation algorithm is designed by adapting different equations from those used for the GOES satellites. The capability of the algorithm is compared with that of existing algorithm applied to the GOES's INR system. The algorithm developed in this paper improves pointing accuracy by 40%, and efficiently compensates the depointings of the imager aiming direction.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1537-1549
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• 2020

The ground sample distance of KOMPSAT-3 is 0.7 m for panchromatic band, 2.8 m for multi-spectral band, and the swath width of KOMPSAT-3 is 16 km. Therefore, an image of an area wider than the swath width (16 km) cannot be acquired with a single scanning. Thus, after scanning multiple areas in units of swath width, the acquired images should be made into one image. At this time, the necessary algorithm is called image mosaicking or image stitching, and is used for cartography. Mosaic algorithm generally consists of the following 4 steps: (1) Feature extraction and matching, (2) Radiometric balancing, (3) Seamline estimation, and (4) Image blending. In this paper, we have studied an effective seamline estimation method for satellite images. As a result, we can estimate the seamline more accurately than the existing method, and the heterogeneity of the mosaiced images was minimized.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1911-1923
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• 2022

The availability of high-resolution satellite images provides precise information without direct observation of the research target. Korea Multi-Purpose Satellite (KOMPSAT), also known as the Arirang satellite, has been developed and utilized for earth observation. The machine learning model was continuously proven as a good classifier in classifying remotely sensed images. This study aimed to compare the performance of the support vector machine (SVM) model in classifying the land cover of the Delaware River port area on high and medium-resolution images. Three optical images, which are KOMPSAT-2, KOMPSAT-3A, and Sentinel-2B, were classified into six land cover classes, including water, road, vegetation, building, vacant, and shadow. The KOMPSAT images are provided by Korea Aerospace Research Institute (KARI), and the Sentinel-2B image was provided by the European Space Agency (ESA). The training samples were manually digitized for each land cover class and considered the reference image. The predicted images were compared to the actual data to obtain the accuracy assessment using a confusion matrix analysis. In addition, the time-consuming training and classifying were recorded to evaluate the model performance. The results showed that the KOMPSAT-3A image has the highest overall accuracy and followed by KOMPSAT-2 and Sentinel-2B results. On the contrary, the model took a long time to classify the higher-resolution image compared to the lower resolution. For that reason, we can conclude that the SVM model performed better in the higher resolution image with the consequence of the longer time-consuming training and classifying data. Thus, this finding might provide consideration for related researchers when selecting satellite imagery for effective and accurate image classification.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.246-249
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• 2005

Satellite ground track display computer program is designed and implemented for the KOMPSAT-2 mission operations. Digitized world map and detailed Korean map is realized with zoom and pan capability. The program supports real-time ground trace and off-line satellite image planning on the world map. Satellite mission timeline is also displayed with the satellite ground track for the visualized mission operations. In this paper, the satellite ground track display is described in the aspect of the functional requirements, design, and implementation.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.125-127
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• 2004

High-resolution satellite images offer abundance information of the earth surface for remote sensing applications. The information includes geometry, texture and attribute characteristic. The pixel-based image classification can't satisfy high-resolution satellite image's classification precision and produce large data redundancy. Object-oriented information extraction not only depends on spectrum character, but also use geometry and structure information. It can provide an accessible and truly revolutionary approach. Using Beijing Spot 5 high-resolution image and object-oriented classification with the eCognition software, we accomplish the cultures' precise classification. The test areas have five culture types including water, vegetation, road, building and bare lands. We use nearest neighbor classification and appraise the overall classification accuracy. The average of five species reaches 0.90. All of maximum is 1. The standard deviation is less than 0.11. The overall accuracy can reach $95.47\%.$ This method offers a new technology for high-resolution satellite images' available applications in remote sensing culture classification.

• Journal of Sensor Science and Technology
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• v.6 no.2
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• pp.106-114
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• 1997

In this paper, we propose an efficient coding method of remotely sensed satellite image using region classification and interband prediction. This method classifies each pixel vector considering spectral characteristics of satellite image data. Then we perform the classified intraband VQ to remove spatial (intraband) redundancy for a reference band image. To remove interband redundancy effectively, we perform the classified interband prediction for the remaining band images. Experiments on LANDSAT TM satellite image show that coding efficiency of the proposed method is better than that of the conventional Gupta's method.