Browse > Article
http://dx.doi.org/10.7780/kjrs.2019.35.5.1.10

A Review of Change Detection Techniques using Multi-temporal Synthetic Aperture Radar Images  

Baek, Won-Kyung (Department of Geoinformatics, University of Seoul)
Jung, Hyung-Sup (Department of Geoinformatics, University of Seoul)
Publication Information
Korean Journal of Remote Sensing / v.35, no.5_1, 2019 , pp. 737-750 More about this Journal
Abstract
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 solar altitude. With the recent increase in the number of SAR satellites, it is possible to acquire images with less than one day temporal resolution for the same area. This advantage greatly increases the availability of change detection for inaccessible areas. Commonly available information in satellite SAR is amplitude and phase information, and change detection techniques have been developed based on each technology. Those are amplitude Change Detection (ACD), Coherence Change Detection (CCD). Each algorithm differs in the preprocessing process for accurate automatic classification technique according to the difference of information characteristics and the final detection result of each algorithm. Therefore, by analyzing the academic research trends for ACD and CCD, each technologies can be complemented. The goal of this paper is identifying current issues of SAR change detection techniques by collecting research papers. This study would help to find the prerequisites for SAR change detection and use it to conduct periodic detection research on inaccessible areas.
Keywords
Change detection; Synthetic Aperture Radar;
Citations & Related Records
Times Cited By KSCI : 14  (Citation Analysis)
연도 인용수 순위
1 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.   DOI
2 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.   DOI
3 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.   DOI
4 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.   DOI
5 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.   DOI
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 Zebker, H. A. and J. Villasenor, 1992. Decorrelation in interferometric radar echoes, IEEE Transactions on Geoscience and Remote Sensing, 30(5): 950-959.   DOI
10 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.   DOI
11 Park, S. E., 2016. Detection of Water Bodies from Kompsat-5 SAR Data, Korean Journal of Remote Sensing, 32(5): 539-550 (in Korean with English abstract).   DOI
12 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.   DOI
13 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).   DOI
14 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.   DOI
15 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.   DOI
16 Bickel, D. L., 2015. On Radar Resolution in Coherent Change Detection (No. SAND2015-10224), Sandia National Lab (SNL-NM), Albuquerque, NM, United States.
17 Even, M. and K. Schulz, 2018. InSAR deformation analysis with distributed scatterers: A review complemented by new advances, Remote Sensing, 10(5): 744.   DOI
18 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.
19 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.   DOI
20 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.   DOI
21 Ferretti, A., F. Novali, R. Bürgmann, 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.
22 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.   DOI
23 Hong, S. H. and S. Wdowinski, 2013. Interferometric coherence analysis using space-borne synthetic aperture radar with respect to spatial resolution, Korean Journal of Remote Sensing, 29(4): 389-397 (in Korean with English abstract).   DOI
24 Hong, S. H. and S. Wdowinski, 2017. A Review on Monitoring the Everglades Wetlands in the Southern Florida Using Space-based Synthetic Aperture Radar (SAR) Observations, Korean Journal of Remote Sensing, 33(4): 377-390.   DOI
25 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.   DOI
26 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.   DOI
27 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).   DOI
28 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.   DOI
29 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.   DOI
30 Bouaraba, A., N. Milisavljevic, M. Acheroy, and D. Closson, 2014. Change Detection and Classification Using High Resolution SAR Interferometry, In Land Applications of Radar Remote Sensing, IntechOpen, https://dx.doi.org/10.5772/57246.   DOI
31 Buades, A., B. Coll, and J. M. Morel, 2011. Non-local means denoising, Image Processing On Line, 1: 208-212.   DOI
32 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.   DOI
33 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.   DOI
34 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).   DOI
35 Hwang, J. I., D. Kim, and H. S. Jung, 2017. An efficient ship detection method for KOMPSAT-5 synthetic aperture radar imagery based on adaptive filtering approach, Korean Journal of Remote Sensing, 33(1): 89-95.   DOI
36 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.   DOI
37 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.   DOI
38 Kang, A. R., Y. G. Byun, and T. B. Chae, 2015. Development and evaluation of a texture-based urban change detection method using very high resolution SAR imagery, Korean Journal of Remote Sensing, 31(3): 255-265 (in Korean with English abstract).   DOI
39 Kim, D. and H. S. Jung, 2017. Oil spill detection from RADARSAT-2 SAR image using non-local means filter, Korean Journal of Remote Sensing, 33(1): 61-67.   DOI
40 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.   DOI
41 Korea Aerospace Research Institute, 2018. Development of Target Recognition Algorithms using KOMPSAT Satellite Images, Korea Aerospace Research Institute, Daejeon, Korea.
42 Kim, D., H. S. Jung, and W. K. Baek, 2016. Comparative analysis among radar image filters for flood mapping, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 34(1): 43-52.   DOI
43 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).   DOI
44 Korea Aerospace Research Institute, 2015. Development of Change Detection Algorithm using High Resolution SAR Images, Korea Aerospace Research Institute, Daejeon, Korea.
45 Lee, K., S. Kang, A. Kim, K. Song, and W. Lee, 2015. Change detection algorithm based on amplitude statistical distribution for high resolution SAR image, Korean Journal of Remote Sensing, 31(3): 227-244 (in Korean with English abstract).   DOI
46 Lee, S., S. H. Park, and H. S. Jung, 2016. Multi-temporal analysis of deforestation in Pyeongyang and Hyesan, North Korea, Korean Journal of Remote Sensing, 32(1): 1-11.   DOI
47 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).   DOI
48 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.   DOI
49 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.
50 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.   DOI
51 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.   DOI
52 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.   DOI
53 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.   DOI
54 Novak, L. M., 2013. Advances in SAR Change Detection, North Atlantic Treaty Organization, https://pdfs.semanticscholar.org/ca6a/1c9304d6c8f336b6b83c5d2c2f1fc67af4a1.pdf?_ga=2.116652038.167531275.1570096722-1930553474.1559270058, Accessed on Oct. 2, 2019.
55 Olmsted, C., 1993. Alaska SAR Facility Scientific SAR User's Guide, Alaska SAR Facility Tech Rep ASF-SD-003, Koyukuk, AK, USA.
56 Ouchi, K., 2004. Principles of synthetic aperture radar for remote sensing, Tokyo Denki, Tokyo, Japan.
57 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.