Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Similarity Measurement using Gabor Energy Feature and Mutual Information for Image Registration

  • Ye, Chul-Soo (Dept. of Ubiquitous IT, Far East University)
  • Received : 2011.10.29
  • Accepted : 2011.11.17
  • Published : 2011.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Image registration is an essential process to analyze the time series of satellite images for the purpose of image fusion and change detection. The Mutual Information (MI) is commonly used as similarity measure for image registration because of its robustness to noise. Due to the radiometric differences, it is not easy to apply MI to multi-temporal satellite images using directly the pixel intensity. Image features for MI are more abundantly obtained by employing a Gabor filter which varies adaptively with the filter characteristics such as filter size, frequency and orientation for each pixel. In this paper we employed Bidirectional Gabor Filter Energy (BGFE) defined by Gabor filter features and applied the BGFE to similarity measure calculation as an image feature for MI. The experiment results show that the proposed method is more robust than the conventional MI method combined with intensity or gradient magnitude.

Keywords

References

  1. Butz, T. and J.P. Thiran, 2001. Affine registration with feature space mutual information, Proc. MICCAI, Lecture Notes in Computer Science, W. Niessen and M. Viergerver, Eds., Berlin, Germany, 2208:549-556.
  2. Cole-Rhodes, A.A., K.L. Johnson, J. LeMoigne, and I. Zavorin, 2003. Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient, IEEE Transactions on Image Processing, 12(12):1495-1511. https://doi.org/10.1109/TIP.2003.819237
  3. Dunn, D., W.E. Higgins, and J. Wakeley, 1994. Texture segmentation using 2D Gabor elementary functions, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(2): 130-149. https://doi.org/10.1109/34.273736
  4. Han, Y.K., D.J. Kim, and Y.I. Kim, 2011. Registration between High-resolution Optical and SAR Images Using Linear Features, Korean Journal of Remote Sensing, 17(2): 141-150. https://doi.org/10.7780/kjrs.2011.27.2.141
  5. Lehureau, G., F. Tupin, C. Tison, G. Oller, and D. Petit, 2008. Registration of metric resolution SAR and optical images in urban areas, European conference on synthetic aperture radar; EUSAR 2008, 197-200.
  6. Suri, S. and P. Reinartz, 2010. Mutual-informationbased registration of TerraSAR-X and Ikonos Imagery in urban areas, IEEE Transactions on Geoscience and Remote Sensing, 48(2): 939-949. https://doi.org/10.1109/TGRS.2009.2034842
  7. Wen, H.Y. and J.T. Gao, 2009. Remote sensing image registration based on mutual information and grey relational analysis, IEEE International Conference on Intelligent Computing and Intelligent Systems, 4: 127-131.
  8. Zitova, B. and J. Flusser, 2003. Image registration methods: a survey, Image and Vision Computing, 21: 977-1000. https://doi.org/10.1016/S0262-8856(03)00137-9

Cited by

  1. Mutual Information-based Circular Template Matching for Image Registration vol.30, pp.5, 2014, https://doi.org/10.7780/kjrs.2014.30.5.1
  2. 이상치 제거와 삼각망 기반의 지역 변환을 이용한 영상 등록 vol.30, pp.6, 2011, https://doi.org/10.7780/kjrs.2014.30.6.9