Integration of optimal spatial distributed tie-points in RANSAC-based image registration

ABSTRACT Feature-based image registration requires the identification of correct tie-points between the image pair. In this paper, an improved outlier method is proposed to find correct matching results of optimal distribution based on RANSAC (RANdom SAmple Consensus) algorithm. The main feature of the proposed method is that an optimal spatial designation of tie-points method using stratified random selection (SRS), is integrated into RANSAC framework to filter out the mismatched features that exist in the massive initial matches generated by SIFT operator in order to estimate mapping function accurately. In this way, the selection of relatively disperse and evenly distributed tie-points based on adaptive stratified partition can make RANSAC efficient. We carried out experiments on the registration of three pairs of satellite images. The proposed SIFT-SRS-RANSAC method leads to higher matching and registration accuracy when comparing with the performance of SIFT-RANSAC and SIFT-bucketing-RANSAC algorithms.

[1]  Dinggang Shen,et al.  Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning , 2016, IEEE Transactions on Biomedical Engineering.

[2]  E. Sertel,et al.  Geometric correction accuracy of different satellite sensor images: application of figure condition , 2007 .

[3]  Roberto Manduchi,et al.  CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data , 2011, Pattern Recognit. Lett..

[4]  Jan Modersitzki,et al.  FAIR: Flexible Algorithms for Image Registration , 2009 .

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

[6]  Shuang Wang,et al.  A deep learning framework for remote sensing image registration , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[7]  Zhang Ying,et al.  Impact of GCP distribution on the rectification accuracy of Landsat TM imagery in a coastal zone , 2006 .

[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]  Hirdesh Kumar,et al.  Face Recognition using SIFT by varying Distance Calculation Matching Method , 2012 .

[10]  Torsten Sattler,et al.  SCRAMSAC: Improving RANSAC's efficiency with a spatial consistency filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[11]  Jiri Matas,et al.  Graph-Cut RANSAC , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  Luís Corte-Real,et al.  Measures for an Objective Evaluation of the Geometric Correction Process Quality , 2009, IEEE Geoscience and Remote Sensing Letters.

[13]  J. J. de Gruijter,et al.  An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means , 2010, Comput. Geosci..

[14]  Kyungdon Joo,et al.  Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution , 2018, Pattern Recognit. Lett..

[15]  Andreas Geiger,et al.  Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[16]  Kaichang Di,et al.  Evaluation and Improvement of Geopositioning Accuracy of IKONOS Stereo Imagery , 2005 .

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

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

[19]  J. W. van Groenigen,et al.  Chapter 14 Designing Spatial Coverage Samples Using the k-means Clustering Algorithm , 2006 .

[20]  L. Young,et al.  Statistical Ecology , 1998, Springer US.

[21]  Ian Dowman,et al.  An improved model for automatic feature-based registration of SAR and SPOT images , 2001 .

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

[23]  Flavio Corradini,et al.  Fair Π , 2007 .

[24]  Yong Xu,et al.  High-Performance SAR Image Matching Using Improved SIFT Framework Based on Rolling Guidance Filter and ROEWA-Powered Feature , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Rahul Sukthankar,et al.  MatchNet: Unifying feature and metric learning for patch-based matching , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[27]  Shanshan Li,et al.  Optimal selection of GCPs from Global Land Survey 2005 for precision geometric correction of Landsat-8 imagery , 2015 .

[28]  Matthew Turk,et al.  EVSAC: Accelerating Hypotheses Generation by Modeling Matching Scores with Extreme Value Theory , 2013, 2013 IEEE International Conference on Computer Vision.

[29]  Rachid Deriche,et al.  A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry , 1995, Artif. Intell..

[30]  Linda J. Young,et al.  Statistical ecology : a population perspective , 2014 .

[31]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.