• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5095-5111
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• 2019

A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.725-735
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• 2016

High-resolution satellite images are used in the fields of mapping, natural disaster forecasting, agriculture, ocean-based industries, infrastructure, and environment, and there is a progressive increase in the development and demand for the applications of high-resolution satellite images. Users of the satellite images desire accurate quality of the provided satellite images. Moreover, the distinguishability of each image captured by an actual satellite varies according to the atmospheric environment and solar angle at the captured region, the satellite velocity and capture angle, and the system noise. Hence , NIIRS must be measured for all captured images. There is a significant deficiency in professional human resources and time resources available to measure the NIIRS of few hundred images that are transmitted daily. Currently, NIIRS is measured every few months or even few years to assess the aging of the satellite as well as to verify and calibrate it [3]. Therefore, we develop an algorithm that can measure the national image interpretability rating scales (NIIRS) of a typical satellite image rather than an artificial target satellite image, in order to automatically assess its quality. In this study, the criteria for automatic edge region extraction are derived based on the previous works on manual edge region extraction [4][5], and consequently, we propose an algorithm that can extract the edge region. Moreover, RER and H are calculated from the extracted edge region for automatic edge region extraction. The average NIIRS value was measured to be 3.6342±0.15321 (2 standard deviations) from the automatic measurement experiment on a typical satellite image, which is similar to the result extracted from the artificial target.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1197-1199
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• 2003

ROCSAT-2 mission is to daily image over Taiwan and the surrounding area for disaster monitoring, land use, and ocean surveillance during the 5-year mission lifetime. The satellite will be launched in December 2003 into its mission orbit, which is selected as a 14 rev/day repetitive Sun-synchronous orbit descending over (120 deg E, 24 deg N) and 9:45 a.m. over the equator with the minimum eccentricity. National Space Program Office (NSPO) is developing a ROCSAT-2 Image Processing System (IPS), which aims to provide real-time high quality image data for ROCSAT-2 mission. A simulated ROCSAT-2 image, based on Level 1B QuickBird Data, is generated for IPS verification. The test image is comprised of one panchromatic data and four multispectral data. The qualification process consists of four procedures: (a) QuickBird image processing, (b) generation of simulated ROCSAT-2 image in Generic Raw Level Data (GERALD) format, (c) ROCSAT-2 image processing, and (d) geometric error analysis. QuickBird standard photogrammetric parameters of a camera that models the imaging and optical system is used to calculate the latitude and longitude of each line and sample. The backward (inverse model) approach is applied to find the relationship between geodetic coordinate system (latitude, longitude) and image coordinate system (line, sample). The bilinear resampling method is used to generate the test image. Ground control points are used to evaluate the error for data processing. The data processing contains various coordinate system transformations using attitude quaternion and orbit elements. Through the qualification test process, it is verified that the IPS is capable of handling high-resolution image data with the accuracy of Level 2 processing within 500 m.

• Korean Journal of Remote Sensing
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• v.7 no.2
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• pp.113-130
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• 1991

Meterological satellites have taken their important place as astandard observing platform from which to measure weather. Specially, they provide a useful information about the weather of wide dessert or sea. This information is really helpful to understand the field of satellite meteorology. Several leading countries, for example, USA, EC, Russia, and Japan, launch two different satellites, both Geostationary and Polar orbiting satellite system. Hewever no technology is developed to our own groundstation for NOAA satellite. The purpose of this paper is to build a home-made NOAA APT groundstation and image processing system to supply this system to secondary school or college.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.509-511
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• 2004

This paper outlines the development of image processing system, which will allow the general users in Government and Public organizations easily to use and apply KOMPSAT EOC images in their own business. The system includes an import/export module of EOC image distributed in Hierarchical Data Format (HDF) file and various image processing analysis modules. Especially, the image mosaic and subset functions are designed to use EOC image as an image map, generating the Ortho-image module. To update the various spatial data with EOC image, some essential modules such as change detection by pattern recognition, overlay between images and vector data, and modification of vector data are implemented in the system. The system is developed based on the user request analysis of government agency, and suited for more efficient use of satellite image in public applications. Such system is expected to contribute to practical application of KOMPSAT-2 that will be launched in 2005. Further efforts will be made to accommodate the KOMPSAT -2 MSC data.

• Journal of Satellite, Information and Communications
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• v.1 no.2
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• pp.76-82
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• 2006

The Mission Control System for KOMPSAT-2 was developed by ETRI and is being operated at Satellite Control Center at KARI to monitor and control KOMPSAT-2 (KOrea Multi-Purpose Satellite) which was launched in July 28th, 2006. MCE provides the functions such as telemetry reception and processing, telecommand generation and transmission, satellite tracking and ranging, orbit prediction and determination, attitude maneuver planning, satellite simulation, etc. KOMPSAT-2 is the successor of KOMPSAT-1 which is an earth-observation satellite. KOMPSAT-2 has higher resolution image taking ability due to MSC (Multi Spectral Camera) payload in the satellite and precise orbit and attitude determination by Mission Control System. It can produce one meter resolution image compared to six meter resolution image by KOMPSAT-1.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.25-30
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• 1999

This paper covers the development of a highly portable and low cost SPOT image data receiving system. We followed two design approaches. One is the software-based approach by which most of the real-time processing is handled by software. With the complete software-based design, it is simple to add a function for receiving any additional satellite data. Satellite-specific format handlers including error correction, decompression and decryption can easily be accommodated. On the other approach. we used a general hardware platform, IBM-PC and a low cost SCSI RAID (Redundant Away of Independent Disks), and therefore, we can make a low cost system.

• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.56-60
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• 2017

This paper describes the design and implementation of fall-down detection algorithm based on image processing. The fall-down detection algorithm separates objects by using background subtraction and binarization after grayscale conversion of the input image acquired by the camera, and recognizes the human body by using labeling operation. The recognized human body can be monitored on the display image, and an alarm is generated when fall-down is detected. By using computer simulation, the proposed algorithm has shown a detection rate of 90%. We verify the feasibility of the proposed system by verifying the function by using the prototype test implemented on the DSP image processing board.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.4
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• pp.1-10
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• 2000

Satellite remote sensing images have much more information compared to a paper map. But these images are generally handled as particular image format gained from optical sensor, and must be processed and analyzed by computer with high priced digital image processing system. For the extraction of digital elevation model(DEM) from satellite image, we used the overlay image by SPOT-3 of Chuncheon area at the Kangwon province. According to the image condition, the precious geometric correction, the bundle adjustment for ortho-image generation and the stereo image mapping by several technical approaches were processed. So that we developed the methods of automatic DEM extraction and efficient stereo image map generation which can improve the digital image processing steps. Also, we applied the multiple direction birdeye view image for modeling and analysis using the remotely sensed overlay images with high resolution.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.