Medium-low resolution multisource remote sensing image registration based on SIFT and robust regional mutual information

ABSTRACT Owing to significant geometric distortions and illumination differences, high precision and robust matching of multisource remote sensing images is a difficult task. To solve this, mutual information (MI)-based methods have been a preferred choice, as MI represents a measure of statistical dependence between the two images. However, MI only considers original grey information and neglects spatial information in the calculation of the probability distribution. In this paper, a novel similarity metric based on rotationally invariant regional mutual information (RIRMI) is proposed. The RIRMI metric is constructed by combining MI with a regional information based on the statistical relationship between rotationally invariant centre-symmetric local binary patterns of the images. The similarity metric based on RIRMI considers not only the spatial information, but the effect of the local grey variations and rotation changes on computing probability density function as well. The proposed method is tested on various simulated remote sensing images (5–30 m GSD) and real remote sensing images (2–30 m GSD) which are taken at different sensors, spectral bands, and times. Results verify that RIRMI is more robust and accurate than the common MI-based registration method.

[1]  Guy Marchal,et al.  Automated multi-moda lity image registration based on information theory , 1995 .

[2]  Amin Sedaghat,et al.  Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Gamini Dissanayake,et al.  L2-SIFT: SIFT feature extraction and matching for large images in large-scale aerial photogrammetry , 2014 .

[4]  Du Kai.  Research on IR and Visual Image Registration Based on Mutual Information and Ant Colony Algorithm , 2008 .

[5]  Maoguo Gong,et al.  A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Zhenhua Wang,et al.  Intensity Order-Based Local Descriptors , 2015 .

[7]  S. Utev.  Central limit theorem for dependent random variables , 1990 .

[8]  Jianglin Ma,et al.  Fully Automatic Subpixel Image Registration of Multiangle CHRIS/Proba Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Hong Wang,et al.  An improvement of image registration based on phase correlation , 2014 .

[10]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

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

[12]  David J. Harding,et al.  Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Zhanyi Hu,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Rotationally Invariant Descript , 2011 .

[14]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

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

[16]  Guy Marchal,et al.  Automated multi-modality image registration based on information theory , 1995 .

[17]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[18]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[19]  Marios S. Pattichis,et al.  Robust Multispectral Image Registration Using Mutual-Information Models , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Amin Sedaghat,et al.  Distinctive Order Based Self-Similarity descriptor for multi-sensor remote sensing image matching , 2015 .

[21]  Eli Shechtman,et al.  Matching Local Self-Similarities across Images and Videos , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Xiao Kai.  Fast Algorithm for Simulated Annealing Applied to Registering of Noisy Images , 2003 .

[23]  Yanrui Ding,et al.  Optimizing the codon usage of synthetic gene with QPSO algorithm. , 2008, Journal of theoretical biology.

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

[25]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[26]  Z. Yi,et al.  Multi-spectral remote image registration based on SIFT , 2008 .

[27]  Jacqueline Le Moigne,et al.  Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient , 2003, IEEE Trans. Image Process..

[28]  Jacqueline Le Moigne,et al.  Agile multi-scale decompositions for automatic image registration , 2016, SPIE Defense + Security.

[29]  Yusheng Xu,et al.  A Novel Subpixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[31]  Carlo Tomasi,et al.  Image Similarity Using Mutual Information of Regions , 2004, ECCV.

[32]  D. Freedman,et al.  Asymptotics of Graphical Projection Pursuit , 1984 .

[33]  Te-Hsiu Sun,et al.  Applying particle swarm optimization algorithm to roundness measurement , 2009, Expert Syst. Appl..

[34]  Jamshid Shanbehzadeh,et al.  An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors , 2013, EURASIP J. Image Video Process..

[35]  Aboul Ella Hassanien,et al.  Remote Sensing Image Registration Based on Particle Swarm Optimization and Mutual Information , 2015 .

[36]  Yuanxin Ye,et al.  A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences , 2014 .

[37]  Pramod K. Varshney,et al.  Performance of mutual information similarity measure for registration of multitemporal remote sensing images , 2003, IEEE Trans. Geosci. Remote. Sens..

[38]  Wenbo Xu,et al.  Adaptive parameter control for quantum-behaved particle swarm optimization on individual level , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[39]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[40]  Kun Gao,et al.  Infrared and visual image registration based on mutual information with a combined particle swarm optimization – Powell search algorithm , 2016 .

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

[42]  Michael T. Orchard,et al.  A fast direct Fourier-based algorithm for subpixel registration of images , 2001, IEEE Trans. Geosci. Remote. Sens..

[43]  Trevor Hastie,et al.  The elements of statistical learning. 2001 , 2001 .

[44]  Amin Sedaghat,et al.  Image Matching of Satellite Data Based on Quadrilateral Control Networks , 2012 .

[45]  Luís Corte-Real,et al.  Automatic Image Registration Through Image Segmentation and SIFT , 2011, IEEE Transactions on Geoscience and Remote Sensing.