A Modified Corner Detector for SAR Images Registration

Article Preview

Abstract:

As a first step for image processing operations, detection of corners is a vital procedure where it can be applied for many applications as feature matching, image registration, image mosaicking, image fusion, and change detection. Image registration can be defined as process of getting the misalignment of pixel's position between two or more images. In this paper, a modified corner detector named Synthetic Aperture Radar-Phase Congruency Harris (SAR-PCH) based on a combination between both phase congruency, named later PC, and Harris corner detector is proposed where PC image can supply fundamental and significative features although the complex changes of intensities. Also, the proposed approach overcomes the Harris limitation concerning the noise since the Harris is more sensitive to the noise. The performance was similitude with Shi-Tomasi, FAST, and Harris corner detectors where experiments are conducted first with simulated images and second with real ones. Mean square error (MSE) and peak signal-to-noise ratio (PSNR) are used for the simile. Experimental results, carried out in a standard computer, verify its effectiveness where it utilizes the privileges of image constitutional depicting, allowing extraction of the most powerful key points since it preserves robustness of co-registration process using image frequency properties which are not variant to illumination. Reasonable results compared to the state of art method as Shi-Tomasi, FAST, and Harris algorithms were achieved on the expense of high computational processing time that can be recovered using hardware having high capabilities.

You might also be interested in these eBooks

Info:

Pages:

123-156

Citation:

Online since:

March 2021

Export:

Price:

* - Corresponding Author

[1] Mahmoud Hassaballah, Khalid M. Hosny, Recent Advances in Computer Vision Theories and Applications, © Springer Nature Switzerland, vol. 804, (2019).

Google Scholar

[2] Ertugrul Bayraktar, Pinar Boyraz, Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics, Turkish Journal of Electrical Engineering & Computer Sciences, 25 (2017) 2444-2454.

DOI: 10.3906/elk-1602-225

Google Scholar

[3] Aboul Ella Hassanien, Mohamed F. Tolba, Khaled Shaalan, Ahmad Taher Azar, Advances in Intelligent Systems and Computing, Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, © Springer Nature Switzerland, 845 (2019).

DOI: 10.1007/978-3-319-99010-1

Google Scholar

[4] Reshmi Krishnan, Anil. A. R., A Survey on Image Matching Methods, International Journal of Latest Research in Engineering and Technology (IJLRET) 2-1 (2016) 58-61.

Google Scholar

[5] Niangang Jiao, Wenchao Kang, Yuming Xianga, Hongjian You, A Novel and Fast Corner Detection Method for SAR Imagery, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII–2/W7 (2017).

DOI: 10.5194/isprs-archives-xlii-2-w7-605-2017

Google Scholar

[6] A.A. Karim, E. F. Nasser, Improvement of Corner Detection Algorithms (Harris, FAST and SUSAN) Based on Reduction of Features Space and Complexity Time, Engineering & Technology Journal, 35-B2 (2017).

DOI: 10.30684/etj.2017.138622

Google Scholar

[7] Ehab Salahat, Murad Qasaimeh, Recent Advances in Features Extraction and Description Algorithms: A Comprehensive Survey, arXiv:1703.06376, 1 (2017).

DOI: 10.1109/icit.2017.7915508

Google Scholar

[8] M. Hassaballah and Ali Ismail Awad, Image Feature Detectors and Descriptors: Foundations and Applications, in: Ali Ismail Awad, Mahmoud Hassaballah (Eds.), Studies in Computational Intelligence, © Springer, Switzerland, 2016, pp.11-47.

DOI: 10.1007/978-3-319-28854-3

Google Scholar

[9] Arthur Ardeshir Goshtasby, Theory and Applications of Image Registration, first ed., JohnWiley & Sons, Inc, (2017).

Google Scholar

[10] Abdelhameed S. Eltanany, M. S. Elwan, and A. S. Amein, Key Point Detection Techniques, in: Aboul Ella Hassanien, Khaled Shaalan, Mohamed Fahmy Tolba (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, Springer Nature, Switzerland, 2020, pp.901-911.

DOI: 10.1007/978-3-030-31129-2_82

Google Scholar

[11] E.R. Davies, Computer Vision Principles, Algorithms Applications, Learning, fifth ed., Elsevier Inc., (2018).

Google Scholar

[12] K Y Kok and P Rajendran, Validation of Harris Detector and Eigen Features Detector, International Conference on Aerospace and Mechanical Engineering (AeroMech17), 370 (2018).

DOI: 10.1088/1757-899x/370/1/012013

Google Scholar

[13] Wenping Ma, Yue Wu, Shaodi Liu, Qingxiu Su, and Yong Zhong, Remote Sensing Image Registration Based on Phase Congruency Feature Detection and Spatial Constraint Matching, IEEE Access 6 (2018) 77554-77567.

DOI: 10.1109/access.2018.2883410

Google Scholar

[14] Zaafouri Ahmed, Mounir Sayadi and Farhat Faniech, Satellite Images features Extraction using Phase Congruency model, International Journal of Computer Science and Network Security (IJCSNS) 9-2 (2009) 192-197.

Google Scholar

[15] Jyoti Malik, G. Sainarayanan and Ratna Dahiya, Corner Detection Using Phase Congruency Features, International Conference on Signal and Image Processing (2010) 217-221.

DOI: 10.1109/icsip.2010.5697472

Google Scholar

[16] Qiang Zhang, Yabin Wang, Long Wang, Registration of images with affine geometric distortion based on maximally stable extremal regions and phase congruency, Image and Vision Computing, (2015).

DOI: 10.1016/j.imavis.2015.01.008

Google Scholar

[17] A.F. Cinar, S.M. Barhli, D. Hollis, M. Flansbjer, R.A. Tomlinson, T.J. Marrow, M. Mostafavi, An autonomous surface discontinuity detection and quantification method by digital image correlation and phase congruency, Optics and Laser Engineering, 96 (2017) 94–106.

DOI: 10.1016/j.optlaseng.2017.04.010

Google Scholar

[18] Clive Trenton, Andrew Lambert, Detecting Space Debris using Phase Congruency, Engineering Project Report, University of New South Wales (UNSW)at Australian Defense Force Academy (DFA) (2017).

Google Scholar

[19] Yuming Xiang, Feng Wang, Ling Wan and Y. Hongjian, SAR-PC: Edge Detection in SAR Images via an Advanced Phase Congruency Model, Remote Sensing-Open Access Journal (MDPI) 9-209 (2017) 1-28.

DOI: 10.3390/rs9030209

Google Scholar

[20] Yuanxin Ye, Jie Shan, Siyuan Hao, Lorenzo Bruzzone, and Yao Qin, A local phase based invariant feature for remote sensing image matching, Journal of Photogrammetry and Remote Sensing (ISPRS) 142 (2018) 205-221.

DOI: 10.1016/j.isprsjprs.2018.06.010

Google Scholar

[21] Mabuza-Hocquet G and Nelwamondo F, Fusion of Phase Congruency and Harris Algorithm for Extraction of Iris Corner Points, Third International Conference on Artificial Intelligence, Modelling and Simulation (2015) 315-320.

DOI: 10.1109/aims.2015.57

Google Scholar

[22] Peter Kovesi, Image Features from Phase Congruency, Technical report 95/4, Department of Computer Science, The University of Western Australia (1995).

Google Scholar

[23] Peter Kovesi, Image Features from Phase Congruency, Journal of Computer Vision Research, 1:3 (1999) 1-27.

Google Scholar

[24] Peter Kovesi, Phase congruency: A low-level image invariant, Psychological Research, 64 (2000) 136-148.

DOI: 10.1007/s004260000024

Google Scholar

[25] Peter Kovesi, Phase Congruency Detects Corners and Edges, in: Sun C., Talbot H., Ourselin S. and Adriaansen T. (Eds.), Proc. VIIth Digital Image Computing: Techniques and Applications, Sydney, 2003, pp.309-318.

DOI: 10.1071/9780643090989

Google Scholar

[26] Zheng Liu and Robert Laganiere, On the Use of Phase Congruency to Evaluate Image Similarity, IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, II (2006) 937-940.

DOI: 10.1109/icassp.2006.1660498

Google Scholar

[27] Adrian Burlacu and Corneliu Lazer, Image Features Detection using Phase Congruency and Its Application in Visual Servoing, Fourth International Conference on Intelligent Computer Communication and Processing (2008) 47-51.

DOI: 10.1109/iccp.2008.4648353

Google Scholar

[28] Y. L. Malathi Latha, Local Feature Integration Method Using Phase Congruency for Palm Print Authentication, International Journal of Image and Graphics 15:3 (2015) 1550008(1-17).

DOI: 10.1142/s0219467815500084

Google Scholar

[29] H.D. Supreetha Gowda and G. Hemantha Kumar, Evaluation of Texture Features for Biometric Verification System Using Handvein and Finger Knuckle print, in: K.C. Santosh, Mallikarjun Hangarge, Vitoantonio Bevilacqua, Atul Negi (Eds.), Communications in Computer and Information Science 709, Springer Nature Singapore Pte Ltd., 2017, pp.420-428.

DOI: 10.1007/978-981-10-4859-3_37

Google Scholar

[30] Zhen Ye, Xiaohua Tong, Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based on Phase Congruency, IEEE Transaction on Geoscience and Remote Sensing (2018).

DOI: 10.1109/tgrs.2018.2870422

Google Scholar

[31] C. Harris and M. Stephens, A combined corner and edge detector, Alvey Vision Conference (1988) 147-151.

DOI: 10.5244/c.2.23

Google Scholar

[32] Moravec, H.P., Towards automatic visual obstacle avoidance, Fifth International Joint Conference on Artificial Intelligence (1977) 584-594.

Google Scholar

[33] Shi, J., and C. Tomasi., Good Features to Track, IEEE Conference on Computer Vision and Pattern Recognition (CVPR94) Seattle (1994).

DOI: 10.1109/cvpr.1994.323794

Google Scholar

[34] Joanna Janicka, Jacek Rapinski, Outliers Detection by RANSAC Algorithm in the Transformation Of 2D Coordinate Frames, Geodetic Sciences Bulletin Online version, 20:3 (2014) 610-625.

DOI: 10.1590/s1982-21702014000300035

Google Scholar

[35] Rosten, E., Drummond, T., Machine learning for high speed corner detection, in: A. Leonardis, H. Bischof, and A. Pinz (Eds.), Ninth European Conference on Computer Vision I, Springer-Verlag Berlin Heidelberg, 2006, pp.430-443.

DOI: 10.1007/11744023_34

Google Scholar

[36] Förstner, W; Gülch, A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centers of Circular Features, International Society for Photogrammetry and Remote Sensing ISPRS, inter-commission workshop, (1987).

Google Scholar