• Current Industrial and Technological Trends in Aerospace
• /
• v.5 no.2
• /
• pp.51-57
• /
• 2007

상용 위성탑재카메라를 이용한 지표면의 고해상도 영상획득은 1990년대부터 세계 각국에서 많은 노력과 투자를 하고 있다. 미국은 이미 해상도 1m급의 IKONOS, Orbview 및 Quickbird 등을 운용하고 있으며 최근에는 해상도 0.5m급 이하의 위성탑재 카메라를 개발하고 있는 것으로 알려졌다. 러시아, 프랑스, 이스라엘, 일본 등도 1m급 탑재카메라를 개발 중이거나 운용중이며 우리나라도 다목적 실용위성 시리즈의 탑재카메라 개발을 통해 고해상도 위성 카메라를 운용 및 개발 중이다. 이러한 개발동향에 따라 고해상도 위성카메라의 광학부품과 구조부품에 대한 기술적 연구와 개발에도 많은 노력이 이루어지고 있는데, 국내에서도 향후 계속되는 국가의 위성탑재카메라 개발계획에 따라 요구되는 핵심 광구조 부품 개발을 위해 대구경 광학 부품이나 구조물에 대해서 단계적인 국내개발을 시작하고 있다. 우선적으로 한국항공우주연구원과 한국표준과학연구원은 연구원간 협력프로그램으로 대구경 우주급 반사경조립체에 대한 국내개발을 진행하고 있으며 우주환경에서의 광학시험에 필요한 관련 시설을 구축하고 있으므로 국내의 위성관련 광구조부품 개발 기술도 획기적으로 향상될 것으로 기대된다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.6
• /
• pp.1279-1284
• /
• 2012

This study is about the change of coastline through using ASTER images. ASTER image is a sensor loaded in earth resources satellite shoot in Japan on Dec. 1999. It has 15m, 30m, 90m coastline, three sensors of VNIR, TIR and WIR, therefore it's possible to obtain more information on the Earth than the existing satellite images cause it contains various a wavelength range in spite of relatively economic image. The coastline is changed according to topography shape because it's strongly localized. Besides, it's one of the most important factors in MGIS(Marine Geographic Information System). Therefore, this study is accomplished by analysing variation after abstraction the coastline automatically by Vector Line from ASTER satellite images. The study result will be used as an important basic data when analysis the change of e coastline hereafter.

• Proceedings of the Optical Society of Korea Conference
• /
• 2000.08a
• /
• pp.36-37
• /
• 2000

정밀 지상관측 위성인 다목적실용위성 1호기에는 해상도 6.6 m인 전자광학카메라(EOC)가 탑재되어 현재 우수한 영상을 보내오고 있으며 2003년 발사예정인 2호기를 위하여 해상도 1 m의 Multispectral Camera(MSC)가 개발중이다. 미 TRW 사가 제작한 EOC 개발에 항우연의 연구진은 그 설계 및 시험의 각 단계별 검토와, 탑재, 위성전체 시험과 보정을 포함한 궤도운용 등의 수행과 함께, 개발기간 동안 현지에서 수행된 별도의 현장교육을 통하여 동급의 위성카메라를 실제 개발할 수 있는 설계기술을 이전받았다. 수차례 대구경 비구면 광학계 제작 경험을 더한 항우연은 MSC 공동개발선인 이스라엘 ELOP 사와 현재 그 설계를 진행하고 있다. (중략)

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.21 no.1
• /
• pp.19-26
• /
• 2003

As recently, high-resolution satellite images of 1m spatial resolution are opened to the public and able to be used commercially, the studies that make ortho-images using them and apply to digital mapping and database of geo-spatial information system are having been progressed actively. Therefore, the purposes of this study are to establish the auto-extraction methods and to develope algorithms for automatically extracting buildings out of man-made structures, after making the IKONOS ortho-image. As the result of this study, we can extract buildings automatically at 72% out of the whole buildings. And we have analyzed the error trend by means of the comparison with ortho-image, digital map and drawing result, then we could know that obtain the good result for extraction of the building through the methods and algorithms of this study.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.24 no.2
• /
• pp.282-297
• /
• 2019

Detection of mesoscale oceanic eddies using satellite data can utilize various ocean parameters such as sea surface temperature (SST), chlorophyll-a pigment concentration in phytoplankton, and sea level altimetry measurements. Observation methods vary for each satellite dataset, as it is obtained using different temporal and spatial resolution, and optimized data processing. Different detection results can be derived for the same oceanic eddies; therefore, fundamental research on eddy detection using satellite data is required. In this study, we used ocean color satellite data, sea level altimetry data, and infrared SST data to detect mesoscale eddies in the East Sea and compared results from different detection methods. The sea surface current field derived from the consecutive ocean color chlorophyll-a concentration images using the maximum cross correlation coefficient and the geostrophic current field obtained from the sea level altimetry data were used to detect the mesoscale eddies in the East Sea. In order to compare the eddy detection from satellite data, the results were divided into three cases as follows: 1) the eddy was detected in both the ocean color and altimeter images simultaneously; 2) the eddy was detected from ocean color and SST images, but no eddy was detected in the altimeter data; 3) the eddy was not detected in ocean color image, while the altimeter data detected the eddy. Through these three cases, we described the difficulties with satellite altimetry data and the limitations of ocean color and infrared SST data for eddy detection. It was also emphasized that study on eddy detection and related research required an in-depth understanding of the mesoscale oceanic phenomenon and the principles of satellite observation.

• Korean Journal of Remote Sensing
• /
• v.33 no.2
• /
• pp.149-158
• /
• 2017

Fine temporal and spatial resolution of image data are necessary to monitor the phenology of vegetation. However, there is no single sensor provides fine temporal and spatial resolution. For solve this limitation, researches on spatiotemporal data fusion methods are being conducted. Among them, FSDAF (Flexible spatiotemporal data fusion) can fuse each band in high accuracy.In thisstudy, we applied MODIS NDVI and Landsat NDVI to enhance time resolution of NDVI based on FSDAF algorithm. Then we proposed the possibility of utilization in vegetation phenology monitoring. As a result of FSDAF method, the predicted NDVI from January to December well reflect the seasonal characteristics of broadleaf forest, evergreen forest and farmland. The RMSE values between predicted NDVI and actual NDVI (Landsat NDVI) of August and October were 0.049 and 0.085, and the correlation coefficients were 0.765 and 0.642 respectively. Spatiotemporal data fusion method is a pixel-based fusion technique that can be applied to variousspatial resolution images, and expected to be applied to various vegetation-related studies.

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

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

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.359-359
• /
• 2023

최근 기후변화로 인한 홍수 및 가뭄과 같은 자연재해가 증가함에 따라 이를 선제적으로 탐지 및 예방할 수 있는 해결책에 대한 필요성이 증가하고 있다. 이러한 수재해를 예방하기 위해서 하천, 저수지 등 가용수자원의 지속적인 모니터링은 필수적이다. SAR 위성 영상의 경우 주야간 및 기상상황에 상관없이 지속적인 수체 탐지가 가능하다. 일반적으로 SAR 기반 수체 탐지 시 송수신 방향이 동일한 편파(co-polarized) 영상을 사용한다. 하지만 co-polarized 영상의 경우 바람 및 강우에 민감하게 반응하여 수체 미탐지의 가능성이 존재한다. 한편 송수신 방향이 서로 다른 편파(cross-polarized) 영상은 강우 및 바람의 영향에 민감하지 않지만 식생에 민감하게 반응하여 수체의 오탐지율이 높다는 단점이 존재한다. 이에 SAR 영상의 편파 특성에 따라 수체 탐지의 정확도 차이가 발생하여 최적의 편파 영상 조합을 구성하는 것이 중요하다. 본 연구에서는 Sentinel-1 SAR 위성의 VV, VH, VV+VH 편파 영상과 머신러닝 알고리즘 중 하나인 SVM (support vector machine)을 활용하여 수체탐지를 수행하였다. 편파 영상 조합별 수체 탐지 결과의 검증을 위하여 혼동행렬 (confusion matrix) 기반 평가지수를 사용하였다. 각각의 수체탐지 결과의 비교 및 분석을 통하여 SAR 기반 수체 탐지를 위한 최적의 밴드 조합을 도출하였다. 본 연구결과를 바탕으로 차후 높은 시공간 해상도를 가진 SAR 영상의 활용이 가능하다면 수재해 및 수자원 관리의 효율성을 높일 수 있을 것으로 기대된다.

• Korean Journal of Remote Sensing
• /
• v.20 no.4
• /
• pp.261-274
• /
• 2004

The feature extraction using remotely sensed imagery has been recognized one of the important tasks in remote sensing applications. As the high-resolution imagery are widely used to the engineering purposes, need of more accurate feature information also is increasing. Especially, in case of the automatic extraction of linear feature such as road using mid or low-resolution imagery, several techniques was developed and applied in the mean time. But quantitatively comparative analysis of techniques and case studies for high-resolution imagery is rare. In this study, we implemented a computer program to perform and compare GDPA (Gradient Direction Profile Analysis) algorithm and Hough transformation. Also the results of applying two techniques to some images were compared with road centerline layers and boundary layers of digital map and presented. For quantitative comparison, the ranking method using commission error and omission error was used. As results, Hough transform had high accuracy over 20% on the average. As for execution speed, GDPA shows main advantage over Hough transform. But the accuracy was not remarkable difference between GDPA and Hough transform, when the noise removal was app]ied to the result of GDPA. In conclusion, it is expected that GDPA have more advantage than Hough transform in the application side.