Robust Affine-Invariant Line Matching for High Resolution Remote Sensing Images

Point-based matching methods usually hold limitation in dealing with low texture scenes. In this paper, a robust affi neinvariant lines matching method is proposed. The method commences with line segments extraction. All the extracted line segments are grouped into salient lines and general lines. Accordingly, the matching procedure includes salient lines matching and general lines matching. In salient lines matching, affi ne invariants are calculated and the matched salient line correspondences are the basis of the general lines matching. Each general line is clustered into a matched salient line according to a certain rule. Taking each salient line as the root, together with all the general lines clustered to it, a control network is constructed. Finally, the general lines matching procedure is performed between the two subnetworks whose roots are correspondences. Experimental results show that our proposed method can successfully process local distortion and improve the matching performance in low texture areas.

[1]  Ramakant Nevatia,et al.  Segment-based stereo matching , 1985, Comput. Vis. Graph. Image Process..

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Cordelia Schmid,et al.  Automatic line matching across views , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Rachid Deriche,et al.  Differential invariants for color images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[6]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

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

[8]  Luc Van Gool,et al.  Wide-baseline stereo matching with line segments , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Rama Chellappa,et al.  Hierarchical stereo and motion correspondence using feature groupings , 1995, International Journal of Computer Vision.

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

[11]  Jan-Michael Frahm,et al.  Feature tracking and matching in video using programmable graphics hardware , 2007, Machine Vision and Applications.

[12]  Vincent Lepetit,et al.  A fast local descriptor for dense matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Q. Du,et al.  Automatic Registration and Mosaicking for Airborne Multispectral Image Sequences , 2008 .

[14]  A. Ardeshir Goshtasby,et al.  Precision Registration and Mosaicking of Multicamera Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Line Eikvil,et al.  Adaptive Registration of Remote Sensing Images using Supervised Learning , 2009 .

[16]  Lu Wang,et al.  Wide-baseline image matching using Line Signatures , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Zhanyi Hu,et al.  MSLD: A robust descriptor for line matching , 2009, Pattern Recognit..

[18]  Davide Marenchino,et al.  Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications , 2009, Sensors.

[19]  B E Kratochvil,et al.  Image‐based 3D reconstruction using helical nanobelts for localized rotations , 2010, Journal of microscopy.

[20]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[21]  Zhanyi Hu,et al.  Robust line matching through line-point invariants , 2012, Pattern Recognit..

[22]  Youkyung Han,et al.  Automatic Registration of High-Resolution Images Using Local Properties of Features , 2012 .