Shape registration for remote-sensing images with background variation

Image registration is a basic and key processing art for multi-sensor and/or multi-temporal remote-sensing images. Generally speaking, multi-temporal images covering the same region may contain different textures and/or changed backgrounds where the classical autonomous registration methods usually fail, especially those before and after disasters. The main reason is that a single pixel with surrounding region becomes unstable and cannot be applied for descriptor generation. However, the topology (or shape) between points should be viewed as steady description. In this article, we introduce a level line and consequent shape recognition algorithm (level line descriptor (LLD)) from close-range applications and propose an improved LLD for remote sensing. We present our analysis based on multi-temporal images with moderately changed backgrounds. The numerical experiments illustrate the operator's potential to become a relatively robust alternative for shape-based remote-sensing registration.

[1]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[2]  Charlie Rothwell.  Object Recognition through Invariant Indexing , 1995 .

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

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

[5]  Jean-Michel Morel,et al.  ASIFT: A New Framework for Fully Affine Invariant Image Comparison , 2009, SIAM J. Imaging Sci..

[6]  Lionel Moisan,et al.  On the Theory of Planar Shape , 2003, Multiscale Model. Simul..

[7]  Yuei-An Liou,et al.  Use of high-resolution FORMOSAT-2 satellite images for post-earthquake disaster assessment: a study following the 12 May 2008 Wenchuan Earthquake , 2010 .

[8]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[9]  Joachim H. Nagel,et al.  Registration of high-resolution images of the retina , 1992, Medical Imaging.

[10]  V. Vohra.  AUTOMATIC REGISTRATION OF IMAGES TO MAPS - THE ARCHANGEL AND ARMIES SYSTEMS , 2000 .

[11]  Luc Van Gool,et al.  Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions , 2000, BMVC.

[12]  Lei Huang,et al.  Feature-based image registration using the shape context , 2010 .

[13]  Lionel Moisan,et al.  Edge Detection by Helmholtz Principle , 2001, Journal of Mathematical Imaging and Vision.

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

[15]  Rama Chellappa,et al.  A computational vision approach to image registration , 1993, IEEE Trans. Image Process..

[16]  Jean-Michel Morel,et al.  From Gestalt Theory to Image Analysis: A Probabilistic Approach , 2007 .

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

[18]  Benjamin Charles Ross,et al.  An analysis of SIFT object recognition with an emphasis on landmark detection , 2004 .

[19]  Kristen Grauman,et al.  Kernelized locality-sensitive hashing for scalable image search , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Cordelia Schmid,et al.  Indexing Based on Scale Invariant Interest Points , 2001, ICCV.

[22]  Farida Cheriet,et al.  Automatic Closed Edge Detection Using Level Lines Selection , 2007, ICIAR.

[23]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[24]  Tania Stathaki,et al.  Image Fusion: Algorithms and Applications , 2008 .

[25]  Lionel Moisan,et al.  A Probabilistic Criterion to Detect Rigid Point Matches Between Two Images and Estimate the Fundamental Matrix , 2004, International Journal of Computer Vision.

[26]  Francesca Bovolo,et al.  A detail-preserving scale-driven approach to change detection in multitemporal SAR images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[27]  B. S. Manjunath,et al.  Registration Techniques for Multisensor Remotely Sensed Imagery , 1996 .

[28]  Andrew W. Moore,et al.  Efficient memory-based learning for robot control , 1990 .

[29]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Yann Gousseau,et al.  Unsupervised thresholds for shape matching , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[31]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[32]  Peter Reinartz,et al.  Applicability of the SIFT operator to geometric SAR image registration , 2010 .

[33]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[34]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.

[35]  Andrew Zisserman,et al.  An Affine Invariant Salient Region Detector , 2004, ECCV.

[36]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[37]  C. Schmid,et al.  Indexing based on scale invariant interest points , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[38]  Jean-Michel Morel,et al.  A Theory of Shape Identification , 2008 .

[39]  Agnès Desolneux,et al.  Vanishing Point Detection without Any A Priori Information , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Lionel Moisan,et al.  Meaningful Alignments , 2000, International Journal of Computer Vision.

[41]  Guangjun Zhang,et al.  An edge-based scale- and affine-invariant algorithm for remote sensing image registration , 2013 .

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