A novel adaptive weights proximity matrix for image registration based on R-SIFT

Abstract We present an efficient and robust algorithm for image registration, which can be used to cope with large geometric and intensity variations between pair of images. There are two main contributions in this paper. First, a new robust scale invariant feature transform descriptor (R-SIFT) is presented, which is invariant under affine transformation. The second contribution is the development of a novel proximity matrix with adaptive weights based on the R-SIFT descriptor, in which the elements of the matrix combine the geometric information of feature points with the gradient information of feature points’ neighborhood. Quantitative comparisons of our algorithm with the related methods show a significant improvement in the presence of large viewpoint changes, scale changes, and illumination contrast. Experimental results for remote sensing image registration show our method outperforms the related methods. Finally, the experimental results of the proposed method applied to the problem of change detection of earthquake induced barrier lake are presented, validating the proposed algorithm.

[1]  H. C. Longuet-Higgins,et al.  An algorithm for associating the features of two images , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[2]  Jan Flusser,et al.  A moment-based approach to registration of images with affine geometric distortion , 1994, IEEE Trans. Geosci. Remote. Sens..

[3]  Jan Flusser,et al.  Pattern recognition by affine moment invariants , 1993, Pattern Recognit..

[4]  Shinji Umeyama,et al.  An Eigendecomposition Approach to Weighted Graph Matching Problems , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[6]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[7]  Wenzhong Shi,et al.  The Line‐Based Transformation Model (LBTM) for image‐to‐image registration of high‐resolution satellite image data , 2006 .

[8]  Ying Yang,et al.  Remote sensing image registration via active contour model , 2009 .

[9]  Jacqueline Le Moigne,et al.  Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery , 2005, IEEE Transactions on Image Processing.

[10]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[11]  Dengrong Zhang,et al.  A fast and fully automatic registration approach based on point features for multi-source remote-sensing images , 2008, Comput. Geosci..

[12]  George C. Stockman,et al.  Matching Images to Models for Registration and Object Detection via Clustering , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Jacqueline Le Moigne,et al.  The Translation Sensitivity of Wavelet-Based Registration , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Tony Lindeberg,et al.  Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure , 1997, Image Vis. Comput..

[16]  Rama Chellappa,et al.  Multisensor image registration by feature consensus , 1999, Pattern Recognit..

[17]  B. S. Manjunath,et al.  A contour-based approach to multisensor image registration , 1995, IEEE Trans. Image Process..

[18]  Yun Zhang,et al.  Wavelet-based image registration technique for high-resolution remote sensing images , 2008, Comput. Geosci..

[19]  Maurizio Pilu,et al.  A direct method for stereo correspondence based on singular value decomposition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[21]  Francesca Odone,et al.  SVD-matching using SIFT features , 2006, Graph. Model..

[22]  Jianping Guo,et al.  Change detection of the Tangjiashan barrier lake based on multi-source remote sensing data , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.