ARRSI: Automatic Registration of Remote-Sensing Images

This paper presents the Automatic Registration of Remote-Sensing Images (ARRSI); an automatic registration system built to register satellite and aerial remotely sensed images. The system is designed specifically to address the problems associated with the registration of remotely sensed images obtained at different times and/or from different sensors. The ARRSI system is capable of handling remotely sensed images geometrically distorted by various transformations such as translation, rotation, and shear. Global and local contrast issues associated with remotely sensed images are addressed in ARRSI using control-point detection and matching processes based on a phase-congruency model. Intensity-difference issues associated with multimodal registration of remotely sensed images are addressed in ARRSI through the use of features that are invariant to intensity mappings during the control-point matching process. An adaptive control-point matching scheme is employed in ARRSI to reduce the performance issues associated with the registration of large remotely sensed images. Finally, a variation on the Random Sample and Consensus algorithm called Maximum Distance Sample Consensus is introduced in ARRSI to improve the accuracy of the transformation model between two remotely sensed images while minimizing computational overhead. The ARRSI system has been tested using various satellite and aerial remotely sensed images and evaluated based on its accuracy and computational performance. The results indicate that the registration accuracy of ARRSI is comparable to that produced by a human expert and improvement over the baseline and multimodal sum of squared differences registration techniques tested

[1]  David A. Clausi,et al.  Automatic registration of SAR and visible band remote sensing images , 2002, IEEE International Geoscience and Remote Sensing Symposium.

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

[3]  Charles V. Stewart,et al.  Robust Parameter Estimation in Computer Vision , 1999, SIAM Rev..

[4]  Andrew Zisserman,et al.  Automated mosaicing with super-resolution zoom , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[5]  Ferran Marqués,et al.  A contour-based approach to automatic and accurate registration of multitemporal and multisensor satellite imagery , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[6]  Venu Madhav Govindu,et al.  Alignment Using Distributions of Local Geometric Properties , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  P Kovesi,et al.  Phase congruency: A low-level image invariant , 2000, Psychological research.

[8]  S. Susan Young.  Alias-free image subsampling using Fourier-based windowing methods , 2004 .

[9]  Mark Hedley,et al.  Fast corner detection , 1998, Image Vis. Comput..

[10]  Faouzi Kossentini,et al.  Fast and high performance image subsampling using feedforward neural networks , 2000, IEEE Trans. Image Process..

[11]  Henry Leung,et al.  A maximum likelihood approach for image registration using control point and intensity , 2004, IEEE Transactions on Image Processing.

[12]  Jeff Orchard,et al.  Efficient Global Weighted Least-Squares Translation Registration in the Frequency Domain , 2005, ICIAR.

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

[14]  Chiou-Ting Hsu,et al.  Multiresolution feature-based image registration , 2000, Visual Communications and Image Processing.

[15]  Peter Kovesi,et al.  Phase Congruency Detects Corners and Edges , 2003, DICTA.

[16]  C. Morandi,et al.  Registration of Translated and Rotated Images Using Finite Fourier Transforms , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Pramod K. Varshney,et al.  Mutual information-based image registration for remote sensing data , 2003 .

[18]  Hui.  A Contour-Based Approach to Multisensor Image Registration , 1995 .

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

[20]  D. Burr,et al.  Feature detection in human vision: a phase-dependent energy model , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.

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

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

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

[24]  Robyn A. Owens,et al.  Feature detection from local energy , 1987, Pattern Recognit. Lett..

[25]  Michael Unser,et al.  Fast parametric elastic image registration , 2003, IEEE Trans. Image Process..

[26]  Peter Kovesi,et al.  Image Features from Phase Congruency , 1995 .

[27]  Siamak Khorram,et al.  A feature-based image registration algorithm using improved chain-code representation combined with invariant moments , 1999, IEEE Trans. Geosci. Remote. Sens..

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

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

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

[31]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .