최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기대한원격탐사학회지 = Korean journal of remote sensing, v.35 no.5 pt.1, 2019년, pp.737 - 750
백원경 (서울시립대학교 공간정보공학과) , 정형섭 (서울시립대학교 공간정보공학과)
Information of target changes in inaccessible areas is very important in terms of national security. Fast and accurate change detection of targets is very important to respond quickly. Spaceborne synthetic aperture radar can acquire images with high accuracy regardless of weather conditions and sola...
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
핵심어 | 질문 | 논문에서 추출한 답변 |
---|---|---|
위성 SAR에서 일반적으로 활용 가능한 정보 두 가지는? | 이러한 장점으로 접근 불능지역에 대한 변화 탐지를 수행할 때 활용성이 크게 증대되었다. 위성 SAR에서 일반적으로 활용 가능한 정보는 강도와 위상 정보로 각각의 기술을 기반으로 변화 탐지 기술이 개발되었다. 강도기반 변화 탐지(ACD; Amplitude Change Detection), 긴밀도 기반 변화 탐지(CCD; Coherence Change Detection). | |
위성 SAR의 장점은? | 접근 불능지역에 대한 표적의 변화 정보는 국가 안보의 측면에서 매우 중요하며 이상 징후에 조속히 대응하기 위해서는 신속하고 정확한 표적의 변화 탐지 결과 도출이 필수적이다. 위성 SAR는 기상 조건과 태양고도에 상관없이 높은 정확도의 영상을 취득할 수 있으며 최근 SAR 위성 수의 증가에 따라 동일 지역에 대하여 1일 미만의 시간 해상도로 영상획득이 가능해졌다. 이러한 장점으로 접근 불능지역에 대한 변화 탐지를 수행할 때 활용성이 크게 증대되었다. | |
변화 탐지와 관련하여 위성 SAR가 가진 매우 높은 장점 두 가지는? | 변화 탐지와 관련하여 위성 SAR(Synthetic Aperture Radar)는 매우 높은 장점을 가지고 있다. 위성 SAR는 마이크로파 대역의 전자기파를 활용하는 능동형 지구관측 위성으로 1) 긴 파장 대역의 전자기파를 활용하기 때문에 대기에 대한 투과율이 높아 광학 위성보다 기상조건에 대한 영향이 적으며, 2) 직접 지표 방향으로 전자기파를 방출한 뒤, 후방 산란(Back scattering) 되어 돌아오는 전자기파를 입력 받아 영상화하기 때문에 주야에 상관없이 영상 자료를 획득할 수 있는 장점이 있다 (Olmsted, 1993; Moreira, 2013). 이러한 특징에 의하여 위성 SAR는 관심 지역에 대하여 지속적인 데이터 취득에 유리하다. |
Ajadi, O., F. Meyer, and P. Webley, 2016. Change detection in synthetic aperture radar images using a multiscale-driven approach, Remote Sensing, 8(6): 482.
Baek, W.-K., H.-S. Jung, S.-H. Chae, and W. J. Lee, 2018a. Two-dimensional Velocity Measurements of Uversbreen Glacier in Svalbard Using TerraSARX Offset Tracking Approach, Korean Journal of Remote Sensing, 34(3): 495-506 (in Korean with English abstract).
Baek, W.-K., H.-S. Jung, and S.-H. Chae, 2018b. Feasibility of ALOS2 PALSAR2 Offset-based Phase Unwrapping of SAR Interferogram in Large and Complex Surface Deformations, IEEE Access, 6(1): 45951-45960.
Baek, W.-K. and H.-S. Jung, 2018c. Precise measurements of the along-track surface deformation related to the 2016 Kumamoto Earthquakes via ionospheric correction of multiple-aperture SAR interferograms, Korean Journal of Remote Sensing, 34(6-4): 1489-1501 (in Korean with English abstract).
Baselice, F., G. Ferraioli, and V. Pascazio, 2013. Markovian change detection of urban areas using very high resolution complex SAR images, IEEE Geoscience and Remote Sensing Letters, 11(5): 995-999.
Bazi, Y., L. Bruzzone, and F. Melgani, 2005. An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images, IEEE Transactions on Geoscience and Remote Sensing, 43(4): 874-887.
Bazi, Y., L. Bruzzone, and F. Melgani, 2007. Image thresholding based on the EM algorithm and the generalized Gaussian distribution, Pattern Recognition, 40(2): 619-634.
Bickel, D. L., 2015. On Radar Resolution in Coherent Change Detection (No. SAND2015-10224), Sandia National Lab (SNL-NM), Albuquerque, NM, United States.
Bouaraba, A., A. Belhadj-Aissa, and D. Closson, 2016. Man-Made Change Detection Using High-Resolution Cosmo-SkyMed SAR Interferometry, Arabian Journal for Science and Engineering, 41(1): 201-208.
Bouaraba, A., A. Belhadj-Aissa, and D. Closson, 2018. Drastic Improvement of Change Detection Results with Multilook Complex SAR Images Approach, Progress In Electromagnetics Research, 82: 55-66.
Buades, A., B. Coll, and J. M. Morel, 2011. Non-local means denoising, Image Processing On Line, 1: 208-212.
Cha, M., R. D. Phillips, P. J. Wolfe, and C. D. Richmond, 2015. Two-stage change detection for synthetic aperture radar, IEEE Transactions on Geoscience and Remote Sensing, 53(12): 6547-6560.
Cui, B., Y. Zhang, L. Yan, and X. Cai, 2017. A SAR intensity images change detection method based on fusion difference detector and statistical properties, Proc. of 2017 ISPRS Geospatial Week, Wuhan, China, Sep. 18-22, vol. IV-2/W4, pp. 439-443.
Di Baldassarre, G., G. Schumann, L. Brandimarte, and P. Bates, 2011. Timely low resolution SAR imagery to support floodplain modelling: a case study review, Surveys in Geophysics, 32(3): 255-269.
Ding, X. and X. Li, 2014. Shoreline movement monitoring based on SAR images in Shanghai, China, International Journal of Remote Sensing, 35(11-12): 3994-4008.
Even, M. and K. Schulz, 2018. InSAR deformation analysis with distributed scatterers: A review complemented by new advances, Remote Sensing, 10(5): 744.
Ferretti, A., F. Novali, R. Burgmann, G. Hilley, and C. Prati, 2004. InSAR permanent scatterer analysis reveals ups and downs in San Francisco Bay area, Eos, Transactions American Geophysical Union, 85(34): 317-324.
Gong, M., Y. Li, L. Jiao, M. Jia, and L. Su, 2014. SAR change detection based on intensity and texture changes, ISPRS Journal of Photogrammetry and Remote Sensing, 93: 123-135.
Hong, S. H., M. J. Jang, S. W. Jung, and S. W. Park, 2018. A Review on Monitoring Mt. Baekdu Volcano Using Space-based Remote Sensing Observations, Korean Journal of Remote Sensing, 34(6-4): 1503-1517 (in Korean with English abstract).
Hwang, J. I. and H. S. Jung, 2018. Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band SAR Satellite Images, Remote Sensing, 10(11): 1799.
Jung, H. S., J. S. Won, and S. W. Kim, 2009. An improvement of the performance of multipleaperture SAR interferometry (MAI), IEEE Transactions on Geoscience and Remote Sensing, 47(8): 2859-2869.
Jung, H. S., S. H. Yun, and M. J. Jo, 2015. An improvement of multiple-aperture SAR inter - ferometry performance in the presence of complex and large line-of-sight deformation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1743-1752.
Kim, D. and H. S. Jung, 2018. Mapping oil spills from dual-polarized SAR images using an artificial neural network: Application to oil spill in the Kerch Strait in November 2007, Sensors, 18(7): 2237.
Kim, Y., D. J. Kim, U. J. Kwon, and H. C. Kim, 2018. A Study on the Radiometric Correction of Sentinel-1 HV Data for Arctic Sea Ice Detection, Korean Journal of Remote Sensing, 34(6-2): 1273-1282 (in Korean with English abstract).
Korea Aerospace Research Institute, 2015. Development of Change Detection Algorithm using High Resolution SAR Images, Korea Aerospace Research Institute, Daejeon, Korea.
Korea Aerospace Research Institute, 2018. Development of Target Recognition Algorithms using KOMPSAT Satellite Images, Korea Aerospace Research Institute, Daejeon, Korea.
Lee, W. J., J.-S. Sun, H. S. Jung, S. C. Park, D. K. Lee, and K.-Y. Oh, 2018 Detection of Surface Changes by the 6th North Korea Nuclear Test Using High-resolution Satellite Imagery, Korean Journal of Remote Sensing, 34(6-4): 1479-1488 (in Korean with English abstract).
Lopez-Martinez, C. and E. Pottier, 2007. Coherence estimation in synthetic aperture radar data based on speckle noise modeling, Applied Optics, 46(4): 544-558.
Monti-Guarnieri, A. V., M. A. Brovelli, M. Manzoni, M. M. d'Alessandro, M. E. Molinari, and D. Oxoli, 2018. Coherent Change Detection for Multipass SAR, IEEE Transactions on Geoscience and Remote Sensing, 56(11): 6811-6822.
Moreira, A., 2013. Synthetic aperture radar (SAR): principles and applications, 4th Advanced Training Course in Land Remote Sensing, https://earth.esa.int/documents/10174/642943/6-LTC2013-SAR-Moreira.pdf, Accessed on Oct. 2, 2019.
Moser, G. and S. B. Serpico, 2006. Generalized minimumerror thresholding for unsupervised change detection from SAR amplitude imagery, IEEE Transactions on Geoscience and Remote Sensing, 44(10): 2972-2982.
Moser, G., S. Serpico, and G. Vernazza, 2007. Unsupervised change detection from multichannel SAR images, IEEE Geoscience and Remote Sensing Letters, 4(2): 278-282.
Nascimento, A. D., A. C. Frery, and R. J. Cintra, 2018. Detecting changes in fully polarimetric SAR imagery with statistical information theory, IEEE Transactions on Geoscience and Remote Sensing, 57(3): 1380-1392.
Novak, L. M., 2013. Advances in SAR Change Detection, North Atlantic Treaty Organization, https://pdfs.semanticscholar.org/ca6a/1c9304d6c8f336b6b83c5d2c2f1fc67af4a1.pdf?_ga2.116652038.167531275.1570096722-1930553474.1559270058, Accessed on Oct. 2, 2019.
Olmsted, C., 1993. Alaska SAR Facility Scientific SAR User's Guide, Alaska SAR Facility Tech Rep ASF-SD-003, Koyukuk, AK, USA.
Ouchi, K., 2004. Principles of synthetic aperture radar for remote sensing, Tokyo Denki, Tokyo, Japan.
Preiss, M. and N. J. Stacy, 2006. Coherent change detection: Theoretical description and experimental results (No. DSTO-TR-1851), Defence Science and Technology Organisation, Edinburgh, Australia.
Rignot, E. J. and J. J. Van Zyl, 1993. Change detection techniques for ERS-1 SAR data, IEEE Transactions on Geoscience and Remote Sensing, 31(4): 896-906.
Suo, Z., Z. Li, and Z. Bao, 2010. A new strategy to estimate local fringe frequencies for InSAR phase noise reduction, IEEE Geoscience and Remote Sensing Letters, 7(4): 771-775.
Touzi, R., A. Lopes, J. Bruniquel, and P. W. Vachon, 1999. Coherence estimation for SAR imagery, IEEE Transactions on Geoscience and Remote Sensing, 37(1): 135-149.
Villasensor, J. D., D. R. Fatland, and L. D. Hinzman, 1993. Change detection on Alaska's North Slope using repeat-pass ERS-1 SAR images, IEEE Transactions on Geoscience and Remote Sensing, 31(1): 227-236.
Wahl, D. E., D. A. Yocky, C. V. Jakowatz, and K. M. Simonson, 2016. A new maximum-likelihood change estimator for two-pass SAR coherent change detection, IEEE Transactions on Geoscience and Remote Sensing, 54(4): 2460-2469.
Wang, S., L. Jiao, and S. Yang, 2016. SAR images change detection based on spatial coding and nonlocal similarity pooling, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8): 3452-3466.
Wang, X., Z. Jia, J. Yang, and N. Kasabov, 2017. Change detection in SAR images based on the logarithmic transformation and total variation denoising method, Remote Sensing Letters, 8(3): 214-223.
Washaya, P., T. Balz, and B. Mohamadi, 2018. Coherence change-detection with sentinel-1 for natural and anthropogenic disaster monitoring in urban areas, Remote Sensing, 10(7): 1026.
Zebker, H. A. and J. Villasenor, 1992. Decorrelation in interferometric radar echoes, IEEE Transactions on Geoscience and Remote Sensing, 30(5): 950-959.
Zhu, Z., 2017. Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications, ISPRS Journal of Photogrammetry and Remote Sensing, 130: 370-384.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.