While SIFT (Scale Invariant Feature Transform) features are used to match High-Resolution (HR) remote sensing urban images captured at different phases with large scale and view variations, feature points are few and the matching accuracy is low. Although replacing SIFT with fully affine invariant features ASIFT (Affine-SIFT) can increase the number of feature points, it results in matching inefficiency and a non-uniform distribution of matched feature point pairs. To address these problems, this paper proposes the novel matching method ICA-ASIFT, which matches HR remote sensing urban images captured at different phases by using an Independent Component Analysis algorithm (ICA) and ASIFT features jointly. First, all possible affine deformations are modeled for the image transform, extracting ASIFT features of remote sensing images captured at different times. The ICA algorithm reduces the dimensionality of ASIFT features and improves matching efficiency of subsequent ASIFT feature point pairs. Next, coarse matching is performed on ASIFT feature point pairs through the algorithms of Nearest Vector Angle Ratio (NVAR), Direction Difference Analysis (DDA) and RANdom SAmple Consensus (RANSAC), eliminating apparent mismatches. Then, fine matching is performed on rough matched point pairs using a Neighborhoodbased Feature Graph Matching algorithm (NFGM) to obtain final ASIFT matching point pairs of remote sensing images. Finally, final matching point pairs are used to compute the affine transform matrix. Matching HR remote sensing images captured at different phases is achieved through affine transform. Experiments are used to compare the performance of ICA-ASFIT and three other algorithms (i.e., Harris- SIFT, PCA-SIFT, TD-ASIFT) on HR remote sensing images captured at different times in different regions. Experimental results show that the proposed ICA-ASFIT algorithm effectively matches HR remote sensing urban images and outperforms other algorithms in terms of matching accuracy and efficiency.
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1. Yin S. W. A Linear Feature-Based Image Rectification Method for HD Remote Sensing Images. - Geomatics Technology and Equipment Vol. 9 2007 No 2 pp. 3-5.
2. Smith S. M. J. M. Brady. SUSAN-A New Approach to Low Level Image Processing. - International Journal of Computer Vision Vol. 23 1997 No 1 pp. 45-78.
3. Harris C. J. M. Stephen. A Combined Corner and Edge Detector. - In: Proc. of 4th Alvey Vision Conference Manchester United Kingdom 1988.
4. Bay H. A. Ess T. Tuytelaars et al. Speeded-Up Robust Features (SURF). - Computer Vision and Image Understanding Vol. 110 2008 No 3 pp. 346-359.
5. David G. L. Distinctive Image Features from Scale-Invariant Key Points. - International Journal of Computer Vision Vol. 60 2004 No 2 91-110.
6. Morel J. M. Y. Guoshen. ASIFT: A New Framework for Fully Affine Invariant Image Comparison. - SIAM Journal on Imaging Sciences Vol. 2 2009 No 2 pp. 438-469.
7. Xu J. J. Y. Zhang H. Zhang. Fast Image Registration Algorithm Based on Improved Harris- SIFT Descriptor. - Journal of Electronic Measurement and Instrumentation 2015 No 1 pp. 48-54.
8. Qiu J. G. J. G. Zhang K. Li. An Image Matching Method Based on Harris and Sift. - Journal of Test and Measurement Technology Vol. 23 2009 No 3 pp. 271-274.
9. Ke Y. R. Sukthanker. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. - In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition Washington 2004.
10. Zhao X. Q. Zhu X. W. Xiao D. R. Li et al. Automatic Matching Method for Aviation Oblique Images Based on Homography Transformation. - Journal of Computer Applications Vol. 35 2015 No 6 pp. 1720-1725.
11. Xiao X. G. D. R. Li B. X. Guo et al. A Rapid Viewpoint Invariant Method for Matching Oblique Images. - Geomatics and Information Science of Wuhan University Vol. 40 2015 No 6 pp. 1-9.
12. Xiao X. G. B. X. Guo D. R. Li et al. A Quick and Affine Invariance Matching Method for Oblique Images. - Acta Geodaetica et Cartographica Sinica Vol. 44 2015 No 4 pp. 414-421.
13. Yang H. S. B. Hong. Principles and Applications of Independent Component Analysis. - Tsinghua University Press Beijing 2006.
14. Hyvarinen A. E. Oja. Independent Component Analysis: Algorithms and Applications. - Neural Networks Vol. 13 2000 No 4/5 pp. 411-430.
15. Tichavsky P. Performance Analysis of the FastICA Algorithm and Cramér-Rao Bounds for Linear Independent Component Analysis. - IEEE Trans Vol. 54 2006 No 4 pp. 1189-1203.
16. Rui T. C. L. Shen Q. Tian J. Ding. Comparison and Analysis on ICA & PCA’s Ability in Feature Extraction. - Pattern Recognition and Artificial Intelligence Vol. 18 2005 No 1 pp. 124-128.
17. Feng Y. M. Y. He J. J. Song J. Wei. ICA-Based Dimensionality Reduction and Compression of Hyperspectral Images. - Journal of Electronics & Information Technology Vol. 29 2007 No 12 pp. 2891-2895.