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http://dx.doi.org/10.7319/kogsis.2013.21.4.101

Automatic Co-registration of Cloud-covered High-resolution Multi-temporal Imagery  

Han, You Kyung (Department of Civil and Environmental Engineering, Seoul National University)
Kim, Yong Il (Department of Civil and Environmental Engineering, Seoul National University)
Lee, Won Hee (Department of Civil Engineering, Chosun University)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.21, no.4, 2013 , pp. 101-107 More about this Journal
Abstract
Generally the commercial high-resolution images have their coordinates, but the locations are locally different according to the pose of sensors at the acquisition time and relief displacement of terrain. Therefore, a process of image co-registration has to be applied to use the multi-temporal images together. However, co-registration is interrupted especially when images include the cloud-covered regions because of the difficulties of extracting matching points and lots of false-matched points. This paper proposes an automatic co-registration method for the cloud-covered high-resolution images. A scale-invariant feature transform (SIFT), which is one of the representative feature-based matching method, is used, and only features of the target (cloud-covered) images within a circular buffer from each feature of reference image are used for the candidate of the matching process. Study sites composed of multi-temporal KOMPSAT-2 images including cloud-covered regions were employed to apply the proposed algorithm. The result showed that the proposed method presented a higher correct-match rate than original SIFT method and acceptable registration accuracies in all sites.
Keywords
Automatic Co-registration; Cloud-covered Imagery; High-resolution Multi-temporal Imagery; SIFT;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Capel, D. and Zisserman, A., 2003, Computer vision applied to super resolution, IEEE Signal Processing Magazine, Vol. 20, No. 3, pp. 75-86.
2 Choi, H. and Bindschadler, R., 2004, Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision, Remote Sensing of Environment, Vol. 91, No. 2, pp. 237-242.   DOI   ScienceOn
3 Fischler, M. A. and Bolles, R. C., 1981, Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communication of the ACM, Vol. 24, No. 6, pp. 381-395.   DOI   ScienceOn
4 Gianinetto, M. and Scaioni, M., 2008, Automated geometric correction of high-resolution pushbroom satellite data, Photogrammetric Engineering and Remote Sensing, Vol. 74, No. 1, pp. 107-116.   DOI   ScienceOn
5 GeoEye, 2006, IKONOS imagery products and product guide, URL: http://www.geoeye.com/Whitepapers_pdfs/GeoEye_ Ikonos_Product_Guide_v17.pdf.
6 Hagolle, O., Huc, M., Pascual, D. V. and Dedieu, G., 2010, A multi-temporal method for cloud detection, applied to FORMOSAT-2, $VEN{\mu}S$, LANDSAT and SENTINEL-2 images, Remote Sensing of Environment, Vol. 114, pp. 1747-1755.   DOI   ScienceOn
7 Han, Y., Byun, Y., Kim, I., 2012, Automatic estimation of geometric translations between high-resolution optical and SAR images, Journal of the Korean Society for Geospatial Information System, Vol. 20, No. 3, pp. 41-48.   과학기술학회마을   DOI   ScienceOn
8 Han, Y., 2013, Automatic image-to-image registration between high-resolution multisensor satellite data in urban areas, Ph.D. dissertation, Seoul National University, Seoul, Korea.
9 Hong, G. and Zhang, Y., 2008, Wavelet-based image registration technique for high-resolution remote sensing images, Computers & Geosciences, Vol. 34, No. 12, pp. 1708-1720.   DOI   ScienceOn
10 Huang, C., Thomas, N., Goward, S. N., Masek, J. G., Zhu, Z., Townshend, J. R. G. and Vogelmann, J. E., 2010, Automated masking of cloud and cloud shadow for forest change analysis using Landsat images, International Journal of Remote Sensing, Vol. 31, No. 20, pp. 5449-5464.   DOI
11 Jin, S., Homer, C., Yang, L., Xian, G., Fry, J., Danielson, P. and Townsend, P., 2013, Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA, International Journal of Remote Sensing, Vol. 34, No. 5 pp. 1540-1560.   DOI
12 Kim, T. and Choi, M., 2009, Image registration of cloudy KOMPSAT-2 imagery using disparity clustering, Korean Journal of Remote Sensing, Vol. 25, No. 3, pp. 287-294.   과학기술학회마을   DOI
13 Lee, W., Yu, S. and Heo, H., 2011, Image registration of cloudy pushbroom scanner images, Korean Journal of Remote Sensing, Vol. 27, No. 1, pp. 9-15.   과학기술학회마을   DOI   ScienceOn
14 Lowe, D., 2004, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, Vol. 60 No. 2, pp. 91-110.   DOI   ScienceOn
15 Yu, L., Zhang, D. and Holden, E., 2008, A fast and fully automatic registration approach based on point features for multi-source remote-sensing images, Computers & Geosciences, Vol. 34, No. 7, pp. 838-848.   DOI   ScienceOn
16 Sedano, F., Kempeneers, P., Strobl, P., Vogt, P., Seebach, L., San-Miguel-Ayanz, L. J., 2012, A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, pp. 588-596.
17 Seo, D., Park, J., Choi, H., Jung, J., Hong, K. and Lee, S., 2013, Characteristics of location accuracy in KOMPSAT-2, Aerospace Engineering and Technology, Vol. 12, No. 1, pp. 144-151.   과학기술학회마을
18 Tseng, D. C., Tseng, H. T. and Chien, C. L., 2008, Automatic cloud removal from multi-temporal SPOT images, Applied Mathematics and Computation, Vol. 205, pp. 584-600.   DOI   ScienceOn