• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.708-708
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• 2002

Recent developments in sensor technologies have provided remotely sensed data with very high spatial resolution. In order to fully utilize the potential of high resolution images, new image classification strategies are necessary. Unfortunately, the high resolution images increase the spectral within-field variability, and the classification accuracy of traditional methods based on pixel-based classification algorithms such as Maximum-Likelihood method may be decreased (Schiewe 2001). Recent development in Object Oriented Classification based on image segmentation algorithms can be used for the classification of forest patches on rugged terrain of Korea. The objectives of this paper are as follows. First, to compare the pros and cons of image classification methods based on pixel-based and object oriented classification algorithm for the forest patch classification. Landsat ETM＋ data and IKONOS data will be used for the classification. Second, to investigate ways to increase classification accuracy of forest patches. Supplemental data such as DTM and Forest Type Map of 1:25,000 scale are used for topographic correction and image segmentation. Third, to propose the best classification strategy for forest patch classification in terms of accuracy and data requirement. The research site for this paper is Namhansansung Provincial Park located at the eastern suburb of Seoul Metropolitan City for its diverse forest patch types and data availability. Both Landsat ETM＋ and IKONOS data are used for the classification. Preliminary results can be summarized as follows. First, topographic correction of reflectance is essential for the classification of forest patches on rugged terrain. Second, object oriented classification of IKONOS data enables higher classification accuracy compared to Landsat ETM＋ and pixel-based classification. Third, multi-stage segmentation is very useful to investigate landscape ecological aspect of forest communities of Korea.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.431-444
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• 2014

As applications of spaceborne SAR imagery are extended, there are increased demands for accurate registrations for better understanding and fusion of radar images. It becomes common to adopt multi-resolution SAR images to apply for wide area reconnaissance. Geometric correction of the SAR images can be performed by using satellite orbit and attitude information. However, the inherent errors of the SAR sensor's attitude and ground geographical data tend to cause geometric errors in the produced SAR image. These errors should be corrected when the SAR images are applied for multi-temporal analysis, change detection applications and image fusion with other sensor images. The undesirable ground registration errors can be corrected with respect to the true ground control points in order to produce complete SAR products. Speeded Up Robust Feature (SURF) technique is an efficient algorithm to extract ground control points from images but is considered to be inappropriate to apply to SAR images due to high speckle noises. In this paper, an attempt is made to apply SURF algorithm to SAR images for image registration and fusion. Matched points are extracted with respect to the varying parameters of Hessian and SURF matching thresholds, and the performance is analyzed by measuring the imaging matching accuracies. A number of performance measures concerning image registration are suggested to validate the use of SURF for spaceborne SAR images. Various simulations methodologies are suggested the validate the use of SURF for the geometric correction and image registrations and it is shown that a good choice of input parameters to the SURF algorithm should be made to apply for the spaceborne SAR images of moderate resolutions.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.123-131
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• 2016

To capture color and near-infrared images simultaneously, a multi-spectral filter array(MSFA) sensor is used. This is because an NIR band gives additional invisible information to human eyes to see subject under extremely low light level. However, because lenses have different refractive indices for different wavelengths, lenses may fail to focus widely different rays to the same convergence point. This is why a chromatic aberration(CA) problem occurs and images are degraded. In this paper, the image restoration algorithm for an MSFA image, which removes the CA problem, is presented. The obtained MSFA image is filtered by the estimated low-pass kernel to generate a base image. This base image is used to remove CA problem in multi-spectral(MS) images. By modeling the image degradation process and by using the least squares approach of the difference between the high-frequencies of the base and MS images, the desired high-resolution MS images are reconstructed. The experimental results show that the proposed algorithm performs well in estimating the high-quality MS images and reducing the chromatic aberration problem.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.10
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• pp.581-586
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• 2005

The multi-beam side scan sonar system uses the towfish installed sonars, it is an equipment that search images of the bottom surface of the sea in real time. It is a typical equipment that is related to a sea investigation such as a geological survey, seabed communication cable and power line cable placing repair investigation, fish breeding ground investigation, sea purification, relic and mineral investigation, and mine and submarine search. Recently, it is used to sea purification and geological survey that require information of the correct surface of the seabed. Therefore, this research develops multi-beam sensors for a side scan sonar that supply various information with the fast scan speed and correct high resolution that is not a simple underwater investigation equipment.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.283-295
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• 2023

With the development of sensor and satellite technology, numerous high-resolution and multi-spectral satellite images have been available. Due to their wavelength-dependent reflection, transmission, and scattering characteristics, multi-spectral satellite images can provide complementary information for earth observation. In particular, the short-wave infrared (SWIR) band can penetrate certain types of atmospheric aerosols from the benefit of the reduced Rayleigh scattering effect, which allows for a clearer view and more detailed information to be captured from hazed surfaces compared to the visible band. In this study, we proposed a multi-resolution transform-based image fusion method to combine visible and SWIR satellite images. The purpose of the fusion method is to generate a single integrated image that incorporates complementary information such as detailed background information from the visible band and land cover information in the haze region from the SWIR band. For this purpose, this study applied the Laplacian pyramid-based multi-resolution transform method, which is a representative image decomposition approach for image fusion. Additionally, we modified the multiresolution fusion method by combining a haze-guided weight map based on the prior knowledge that SWIR bands contain more information in pixels from the haze region. The proposed method was validated using very high-resolution satellite images from Worldview-3, containing multi-spectral visible and SWIR bands. The experimental data including hazed areas with limited visibility caused by smoke from wildfires was utilized to validate the penetration properties of the proposed fusion method. Both quantitative and visual evaluations were conducted using image quality assessment indices. The results showed that the bright features from the SWIR bands in the hazed areas were successfully fused into the integrated feature maps without any loss of detailed information from the visible bands.

• Korean Journal of Geomatics
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• v.2 no.1
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• pp.47-55
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• 2002

Remote sensing is the science and art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation./sup 1)/ EOC (Electro -Optical Camera) sensor loaded on the KOMPSAT-1 (Korea Multi- Purpose Satellite-1) performs the earth remote sensing operation. EOC can get high-resolution images of ground distance 6.6m during photographing; it is possible to get a tilt image by tilting satellite body up to 45 degrees at maximum. Accordingly, the device developed in this study enables to obtain images by photographing one pair of tilt image for the same point from two different planes. KOMPSAT-1 aims to obtain a Korean map with a scale of 1:25,000 with high resolution. The KOMPSAT-1 developed automated feature extraction system based on stereo satellite image. It overcomes the limitations of sensor and difficulties associated with preprocessing quite effectively. In case of using 6, 7 and 9 ground control points, which are evenly spread in image, with 95% of reliability for horizontal and vertical position, 3-dimensional positioning was available with accuracy of 6.0752m and 9.8274m. Therefore, less than l0m of design accuracy in KOMPSAT-1 was achieved. Also the ground position error of ortho-image, with reliability of 95%, is 17.568m. And elevation error showing 36.82m was enhanced. The reason why elevation accuracy was not good compared with the positioning accuracy used stereo image was analyzed as a problem of image matching system. Ortho-image system is advantageous if accurate altitude and production of digital elevation model are desired. The Korean map drawn on a scale of 1: 25,000 by using the new technique of KOMPSAT-1 EOC image adopted in the present study produces accurate result compared to existing mapping techniques involving high costs with less efficiency.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.401-404
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• 2004

SAR (Synthetic Aperture Radar) signal data need a focusing procedure to make the information available to the user. In recent SAR systems, various sensing modes and mission operations are applied to acquire high-resolution SAR images. Therefore, in order to develop generalized focusing software for multi-satellites, a regularized parameter configuration that sufficiently represents sensor and platform characteristics of the SAR system is required. The objective of this paper is to introduce the consideration of parameter definition for developing a generalized SAR processor and to discuss the flexibility and extensibility of defined parameters. The proposed parameter configuration can be applied to a SAR processor. Experiments based on real data will show the suitability of the suggested processing parameters.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.561-565
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• 2011

This paper presents an imaging system for reconstruction of enhanced color images using the conventional Bayer CFA. By extracting various colors such as RGBCY from two sequential images which consist of a image by broadband G channel lens filter and the other image captured without one, the proposed color image reconstruction system can reduce the computational complexity for demosaicking and make high resolution color information without aliasing artifacts. Because the proposed system uses the common Bayer CFA image sensor, fabricating a new type of CFA is not necessary for obtaining a multi-spectral image, which can be easily extensible for applications of multi-spectral imaging. Finally, in order to verify the performance of the proposed system, experimental results are performed. By comparing with the existing demosaicking methods, the proposed camera system showed the significant improvements in the sense of color resolution.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.636-641
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• 2018

Recently, the digital signages display not only the rectangular shapes but also the various shapes and sizes. The high-resolution large-screen display monitors have evolved to multi-vision modes in which several screens are connected to one another. In this paper, we present the structure of an atypically shaped signage system in which the ROI changes with the rotation of the multiple displays. The inclination angle of the monitor is calculated by taking the output value of the gyro sensor inter-locked with the Arduino, and an image in which the position of the four corners is varied according to the rotation angle by using the polar coordinate system. In order to display images in the multi-screen environment, multiple displays with a gyro sensor were controlled using serial communication. As the result, we have obtained the flexibly moving monitor systems with associated images fitting in them.