Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

RNCC-based Fine Co-registration of Multi-temporal RapidEye Satellite Imagery

RNCC 기반 다시기 RapidEye 위성영상의 정밀 상호좌표등록

  • Han, Youkyung (School of Convergence & Fusion System Engineering, Kyungpook National University) ;
  • Oh, Jae Hong (Department of Civil Engineering, Korea Maritime and Ocean University)
  • Received : 2018.11.23
  • Accepted : 2018.12.10
  • Published : 2018.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The aim of this study is to propose a fine co-registration approach for multi-temporal satellite images acquired from RapidEye, which has an advantage of availability for time-series analysis. To this end, we generate multitemporal ortho-rectified images using RPCs (Rational Polynomial Coefficients) provided with RapidEye images and then perform fine co-registration between the ortho-rectified images. A DEM (Digital Elevation Model) extracted from the digital map was used to generate the ortho-rectified images, and the RNCC (Registration Noise Cross Correlation) was applied to conduct the fine co-registration. Experiments were carried out using 4 RapidEye 1B images obtained from May 2015 to November 2016 over the Yeonggwang area. All 5 bands (blue, green, red, red edge, and near-infrared) that RapidEye provided were used to carry out the fine co-registration to show their possibility of being applicable for the co-registration. Experimental results showed that all the bands of RapidEye images could be co-registered with each other and the geometric alignment between images was qualitatively/quantitatively improved. Especially, it was confirmed that stable registration results were obtained by using the red and red edge bands, irrespective of the seasonal differences in the image acquisition.

본 연구는 다시기 영상의 활용이 유리한 RapidEye 영상의 활용성을 증대시키기 위하여, 이들 간에 지역적으로 존재하는 기하오차를 최소화 하는 정밀 상호좌표등록 기법을 제안하였다. 이를 위해, RapidEye 영상과 함께 제공되는 RPCs (Rational Polynomial Coefficients)를 이용하여 다시기 정사영상을 생성하고, 정사영상 간의 정밀 상호 좌표등록을 수행하였다. 정사영상을 생성하기 위해서 수치지도에서 추출된 DEM (Digital Elevation Model)을 활용하였으며, 정밀 상호좌표등록을 수행하기 위하여 RNCC (Registration Noise Cross Correlation) 기법을 적용하였다. 영광지역에 대해 2015년 5월부터 2016년 11월까지 획득된 RapidEye 1B 영상 총 4장을 활용하여 실험을 진행하였으며, 밴드별(blue, green, red, red edge, near-infrared)로 적용된 정밀 상호좌표등록 결과 비교분석을 통해 각 밴드가 보이는 상호좌표등록 적용 가능성 여부를 판단하였다. 실험 결과, RapidEye 영상의 모든 밴드를 활용하여 상호좌표등록이 가능하였으며, 상호좌표등록을 하지 않았을 때보다 다시기 영상 간 정량적/정성적으로 향상된 기하 일치도를 보였다. 특히 red와 red edge 밴드를 이용할 경우 다시기 영상 촬영시기의 계절적 차이에 관계없이 안정적인 상호좌표등록 결과를 보임을 확인하였다.

Keywords

GCRHBD_2018_v36n6_581_f0001.png 이미지

Fig. 1. Tested RapidEye images (band 1)

GCRHBD_2018_v36n6_581_f0002.png 이미지

Fig. 2. The contours of the 1:25,000 digital maps (a) and generated DEM of 20 meters resolution (b) on the internet image map

GCRHBD_2018_v36n6_581_f0003.png 이미지

Fig. 3. RapidEye image overlaid with base image map before (a) and after (b) the orthorectification

GCRHBD_2018_v36n6_581_f0004.png 이미지

Fig. 4. Extracted conjugate points from the red bands of 20150520 and 20160419 image pair (Determined segments are expressed as white rectangles, and the corresponding points located at centroids of the segments are marked as red crosses)

GCRHBD_2018_v36n6_581_f0005.png 이미지

Fig. 5. Visual inspection of fine co-registration results applied by red bands: (a) before co-registration and (b) after co-registration

Table 1. Specification of tested RapidEye 1B images

GCRHBD_2018_v36n6_581_t0001.png 이미지

Table 2. Accuracy assessment of co-registration results focusing on multispectral bands

GCRHBD_2018_v36n6_581_t0002.png 이미지

References

  1. BlackBridge (2013), Satellite image product specifications, BlackBridge, Berlin, Germany, http://blackbridge.com/rapideye/upload/RE_Product_Specifications_ENG.pdf (last date accessed: 5 Oct 2014).
  2. Carson, C., Belongie, S., Greenspan, H., and Malik, J. (2002), Blobworld: Image segmentation using expectationmaximization and its application to image querying, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 8, pp. 1026-1038. https://doi.org/10.1109/TPAMI.2002.1023800
  3. Goshtasby, A. (1986), Piecewise linear mapping functions for image registration, Pattern Recognition, Vol. 19, No. 6, pp. 459-466. https://doi.org/10.1016/0031-3203(86)90044-0
  4. Han, Y. (2017), Fine registration between very high resolution satellite images using registration noise distribution, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 3, pp. 125-132. (in Korean with English abstract) https://doi.org/10.7848/KSGPC.2017.35.3.125
  5. Han, Y., Choi, J., Byun, Y., and Kim, Y. (2014), Parameter optimization for the extraction of matching points between high-resolution multisensory images in urban areas, IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 9, pp. 5612-5621. https://doi.org/10.1109/TGRS.2013.2291001
  6. Han, Y. and Oh, J. (2018), Automated geo/co-registration of multi-temporal very-high-resolution imagery, Sensors, Vol. 18, No. 5, pp. 1599. https://doi.org/10.3390/s18051599
  7. Hong, G. and Zhang, Y. (2008), Wavelet-based image registration technique for high-resolution remote sensing images, Computers and Geosciences, Vol. 34, No. 12, pp. 1708-1720. https://doi.org/10.1016/j.cageo.2008.03.005
  8. Oh, J. and Lee, C. (2014), Automated improvement of RapidEye 1-B geo-referencing accuracy using 1: 25,000 digital maps, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 5, pp. 505-513. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2014.32.5.505
  9. Samet, H. (1984), The quadtree and related hierarchical data structures, Computing Surveys, Vol. 16, No. 2, pp. 187-230. https://doi.org/10.1145/356924.356930
  10. Ye, Y. and Shan, J. (2014), A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 90, No. 4, pp. 83-95. https://doi.org/10.1016/j.isprsjprs.2014.01.009
  11. Zhu, X., Cao, H., Zhang, Y., Tan, K., and Ling, X. (2018), Fine registration for VHR images based on superpixel registration-noise estimation, IEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 10, pp. 1615-1619.
  12. Zitova, B. and Flusser, J. (2003), Image registration methods: a survey, Image and Vision Computing, Vol. 21, No. 11, pp. 977-1000. https://doi.org/10.1016/S0262-8856(03)00137-9