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Automated Algorithm for Super Resolution(SR) using Satellite Images

위성영상을 이용한 Super Resolution(SR)을 위한 자동화 알고리즘

  • Received : 2018.03.22
  • Accepted : 2018.04.06
  • Published : 2018.04.30

Abstract

High-resolution satellite imagery is used in diverse fields such as meteorological observation, topography observation, remote sensing (RS), military facility monitoring and protection of cultural heritage. In satellite imagery, low-resolution imagery can take place depending on the conditions of hardware (e.g., optical system, satellite operation altitude, image sensor, etc.) even though the images were obtained from the same satellite imaging system. Once a satellite is launched, the adjustment of the imaging system cannot be done to improve the resolution of the degraded images. Therefore, there should be a way to improve resolution, using the satellite imagery. In this study, a super resolution (SR) algorithm was adopted to improve resolution, using such low-resolution satellite imagery. The SR algorithm is an algorithm which enhances image resolution by matching multiple low-resolution images. In satellite imagery, however, it is difficult to get several images on the same region. To take care of this problem, this study performed the SR algorithm by calibrating geometric changes on images after applying automatic extraction of feature points and projection transform. As a result, a clear edge was found just like the SR results in which feature points were manually obtained.

고해상도 위성영상은 기상관측, 지형관측, 원격탐사, 군사시설감시, 문화재보호 등 많은 분야에서 이용된다. 위성영상은 동일한 위성영상 시스템에서 획득한 영상이라 할지라도 하드웨어(광학장치, 위성의 운용고도, 영상 센서 등)의 조건에 따라서 해상도가 저하된 영상들이 발생한다. 따라서 위성이 발사된 이후에는 이러한 해상도가 저하된 영상들의 해상도 향상을 위해서 영상시스템의 하드웨어를 변경하는 것은 불가능하므로 위성영상 자체를 이용하여 해상도를 향상시키는 방법이 필요하다. 본 논문에서는 이러한 저해상도 위성영상을 이용하여 해상도를 향상시키는 방법으로 SR(Super Resolution) 알고리즘을 사용하였다. SR 알고리즘은 다수의 저해상도 영상들의 정합을 통해 영상의 해상도를 향상시키는 알고리즘이다. 하지만 위성영상에서는 동일 지역에 대한 여러 장의 영상을 획득하기 어렵다. 따라서 본 논문에서는 이러한 문제점을 해결하기 위해 특징점 자동추출과 투영 변환(Projection Transform)을 적용 후 영상에 대한 기하학적 변화를 보정하여 SR 알고리즘을 수행하였다. 그 결과 수동으로 특징점을 구한 SR 결과와 같이 에지 부분이 뚜렷하게 나타나는 것을 확인 할 수 있다.

Keywords

References

  1. J. Y. Kang, I. l. Kim, J. H. Kim, and J. W. Park, "Enhancement of Spatial Resolution to Local Area for High Resolution Satellite Imagery,"Journal of the Institute of Electronics Engineers of Korea, Vol. 50, no. 4, pp. 897-903, 2013.
  2. R. C. Gonzalez, R. E. Woods, Digital Image Processing 2nd edition, 2002.
  3. S. C.l Park, M. K. Park, and M. G. Kang, "Super-Resolution Image Construction: A Technical Overview," IEEE signal processing magazine, vol. 20, no. 3, pp. 21-36, 2003.. https://doi.org/10.1109/MSP.2003.1203207
  4. Superresolution using Papoulis-Gerchberg Algorithm, EE392J-Digital Video Processing, Stanford University, Stanford, CA, http://www.stanford.edu/class/ee392j/Winter2004/projects/ Deepesh/ee392j-project.doc(accessed Dec., 24, 2017).
  5. M. Irani and S. Peleg, "Improving resolution by image registration," CVGIP: Graphical Models and Image Proc., vol. 53, pp. 231-239, 1991. https://doi.org/10.1016/1049-9652(91)90045-L
  6. A. Zomet, A. Rav-Acha and S. Peleg, "Robust Super-Resolution," IEEE Computer Vision and Pattern Recognition, vol. 1, pp. 645-650, 2001.
  7. A. M. Tekalp, Digital Video Processing. Englewood Cliffs, NJ: Prentice Hall, 1995.
  8. T. Q. Pham, L. J. van Vliet and K. Schutte, "Robust fusion of irregularly sampled data using adaptive normalized convolution", EURASIP J. Appl. Signal Process., vol. 2006, pp. 1-12, 2006.
  9. C. Harris and M. Stephens. "A combined comer and edge detector," In Alvey Vision Conference, pp. 147-151, 1988.
  10. D. Tsai and C. Lin, "Fast normalized cross correlation for defect detection," Pattern Recognit. Lett. vol. 24, no. 15, pp. 2625-2631, 2003. https://doi.org/10.1016/S0167-8655(03)00106-5
  11. P. H. S. Torr and A. Zisserman, "MLESAC: A New Robust Estimator with Application to Estimating Image Geometry", Computer Vision and Image Understanding, vol. 78, pp. 138-156, 2000. https://doi.org/10.1006/cviu.1999.0832
  12. S. B. Jang and I. h. Jee, "A study on fast stero matching algorithm using Belief Propagation in multi-resolution domain," Journal of The Institute of Internet, Broadcasting and Communication (JIIBC) No, 4, pp. 67-73, Aug. 2008.