Browse > Article
http://dx.doi.org/10.7780/kjrs.2021.37.5.1.2

Feasibility Study on FSIM Index to Evaluate SAR Image Co-registration Accuracy  

Kim, Sang-Wan (Department of Energy Resources and Geosystems Engineering, Sejong University)
Lee, Dongjun (Department of Geoinformation Engineering, Sejong University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.5_1, 2021 , pp. 847-859 More about this Journal
Abstract
Recently, as the number of high-resolution satellite SAR images increases, the demand for precise matching of SAR imagesin change detection and image fusion is consistently increasing. RMSE (Root Mean Square Error) values using GCPs (Ground Control Points) selected by analysts have been widely used for quantitative evaluation of image registration results, while it is difficult to find an approach for automatically measuring the registration accuracy. In this study, a feasibility analysis was conducted on using the FSIM (Feature Similarity) index as a measure to evaluate the registration accuracy. TerraSAR-X (TSX) staring spotlight data collected from various incidence angles and orbit directions were used for the analysis. FSIM was almost independent on the spatial resolution of the SAR image. Using a single SAR image, the FSIM with respect to registration errors was analyzed, then use it to compare with the value estimated from TSX data with different imaging geometry. FSIM index slightly decreased due to the differencesin imaging geometry such as different look angles, different orbit tracks. As the result of analyzing the FSIM value by land cover type, the change in the FSIM index according to the co-registration error was most evident in the urban area. Therefore, the FSIM index calculated in the urban was mostsuitable for determining the accuracy of image registration. It islikely that the FSIM index has sufficient potential to be used as an index for the co-registration accuracy of SAR image.
Keywords
SAR; Registration; Accuracy; FSIM; TerraSAR-X;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Brigot, G., E. Colin-Koeniguer, A. Plyer, and J. Fabrice, 2016. Adaptation and evaluation of an optical flow method applied to coregistration of forest remote sensing images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(7): 2923-2939.   DOI
2 Wang, Z., A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, 2004. Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 13(4): 600-612.   DOI
3 Zhang, L., L. Zhang, X. Mou, and D. Zhang, 2011. FSIM: A feature similarity index for image quality assessment, IEEE Transactions on Image Processing, 20(8): 2378-2386.   DOI
4 Concetta Morrone, M. and D.C. Burr, 1988. Feature detection in human vision: A phase-dependent energy model, Proceedings of the Royal Society of London, Series B. Biological Sciences, 235(1280): 221-245.
5 Dellinger, F., J. Delon, Y. Gousseau, J. Michel, and F. Tupin, 2015. SAR-SIFT: a SIFT-like algorithm for SAR images, IEEE Transactions on Geoscience and Remote Sensing, 53(1): 453-466.   DOI
6 Goncalves, H., J.A. Goncalves, and L. Corte-Real, 2009. Measures for an objective evaluation of the geometric correction process quality, IEEE Geoscience and Remote Sensing Letters, 6(2): 292-296.   DOI
7 Sampat, M.P., Z. Wang, S. Gupta, A.C. Bovik, and M.K. Markey, 2009. Complex wavelet structural similarity: A new image similarity index, IEEE Transactions on Image Processing, 18(11): 2385-2401.   DOI
8 Wang, Z. and A.C. Bovik, 2009: Mean squared error: Love it or leave it? A new look at signal fidelity measures, IEEE Signal Processing Magazine, 26(1): 98-117.   DOI
9 Wang, Z., E.P. Simoncelli, and A.C. Bovik, 2003. Multiscale structural similarity for image quality assessment, Proc. of The Thrity-Seventh Asilomar Conference on Signals, Systems and Computers, 2003, Pacific Grove, CA, Nov. 9-12, vol. 2, pp. 1398-1402.
10 Sara, U., M. Akter, and M.S. Uddin, 2019. Image quality assessment through FSIM, SSIM, MSE and PSNR-a comparative study, Journal of Computer and Communications, 7(3): 8-18.   DOI
11 Chandler, D.M. and S.S. Hemami, 2007. VSNR: A wavelet-based visual signal-to-noise ratio for natural images, IEEE Transactions on Image Processing, 16(9): 2284-2298.   DOI
12 Kovesi, P., 1999, Image features from phase congruency, Videre: Journal of Computer Vision Research, 1(3): 1-26.
13 Field, D.J., 1987. Relations between the statistics of natural images and the response properties of cortical cells, Journal of the optical Society of America A, 4(12): 2379-2394.   DOI
14 Jahne, B., H. Haussecker, and P. Geissler (Eds.), 1999. Handbook of computer vision and applications, Academic press, Cambridge, MA, USA.
15 Wang, Z. and A.C. Bovik, 2006. Modern image quality assessment. Synthesis Lectures on Image, Video, and Multimedia Processing, Modern Image Quality Assessment, Morgan & Claypool, San Rafael, CA, USA.