volume | PIER Journals

2013-12-01

A Quantitative Evaluation Method of Ground Control Points for Remote Sensing Image Registration

Wenting Ma,

Dr. Wenting Ma

Department of Electronic Engineering

Tsinghua University

China

Homepage

Jian Yang,

Dr. Jian Yang

Department of Electronic Engineering

Tsinghua University

China

Homepage

Xia Ning,

Dr. Xia Ning

Department of Electronic Engineering

Tsinghua University

China

Homepage

Wei Gao

Dr. Wei Gao

Department of Electronic Engineering

Tsinghua University

China

Homepage

Progress In Electromagnetics Research M, Vol. 34, 55-62, 2014

doi:10.2528/PIERM13092902 | Google Scholar

Abstract

Ground control point (GCP) extraction is an essential step in automatic registration of remote sensing images. However the lack of quantitative and objective methods for analyzing GCP quality becomes the bottleneck that prevents the broad development of automatic image registration. Although several measurements for evaluating the number, accuracy and distribution of GCPs have been proposed in recent years, some of them are redundant and the evaluation of dispersion is not effective enough. In this paper, a method for an objective and quantitative evaluation of GCP quality is proposed. The proposed method consists of three parts: measurement calculation, cost function calculation and final validation. In the first part, two new measurements are proposed to evaluate the number, dispersion and isotropy of GCPs, and the root mean square of GCP residuals using leave-one-out method (RMSloo) is used to evaluate the accuracy. In the second part, seed cost functions are utilized to transform the measurements into a limited value range as well as to be desired on the ascending direction. Subsequently, all the seed cost functions are combined by a total cost function to provide an integrated evaluation. In the third part, the GCP scenario is validated by the accepted threshold depending on the value of the total cost function. To evaluate the performance of the proposed method, experiments using four typical emulated scenarios of GCP distribution and two sets of real GCPs in SAR images are considered. The results demonstrate that the proposed GCP evaluation method performs more effectively than the existing methods, especially in the evaluation of dispersion quality.

Download Full Article (618) View Full Article volume

Citation

Copy Export

Wenting Ma, Jian Yang, Xia Ning, and Wei Gao, "A Quantitative Evaluation Method of Ground Control Points for Remote Sensing Image Registration," Progress In Electromagnetics Research M, Vol. 34, 55-62, 2014.
doi:10.2528/PIERM13092902

References

1. Fan, B., C. Huo, C. Pan, and Q. Kong, "Registration of optical and SAR satellite images by exploring the spatial relationship of the improved SIFT," IEEE Geosci. Remote Sens. Lett, Vol. 10, No. 4, 657-661, 2013.
doi:10.1109/LGRS.2012.2216500 Google Scholar

2. Moser, G. and S. B. Serpico, "Unsupervised change detection from multichannel SAR data by Markovian data fusion," IEEE Trans. on Geosci. Remote Sens, Vol. 47, No. 7, 2114-2128, 2009.
doi:10.1109/TGRS.2009.2012407 Google Scholar

3. Alparone, L., S. Baronti, A. Garzelli, and F. Nencini, "Landsat ETM+ and SAR image fusion based on generalized intensity modulation," IEEE Trans. on Geosci. Remote Sens., Vol. 42, No. 12, 2832-2839, 2004.
doi:10.1109/TGRS.2004.838344 Google Scholar

4. Du, L., H. Liu, Z. Bao, and M. Xing, "Radar HRRP target recognition based on higher order spectra," IEEE Trans. on Signal Process., Vol. 53, No. 7, 2359-2368, 2005.
doi:10.1109/TSP.2005.849161 Google Scholar

5. Ma, W. and J. Yang, "Target detection algorithm for polarimetric SAR images using GOPCE," Proc. IEEE CIE Int. Conf. Radar, 507-509, 2011. Google Scholar

6. Chureesampant, K. and J. Susaki, "Automatic GCP extraction of fully polarimetric SAR images," IEEE Trans. on Geosci. Remote Sens., Vol. 52, No. 1, 137-148, 2014.
doi:10.1109/TGRS.2012.2236890 Google Scholar

7. Gon»calves, H., J. A. Gon»calves, and L. Corte-Real, "Measures for an objective evaluation of the geometric correction process quality," IEEE Geosci. Remote Sens. Lett., Vol. 6, No. 2, 292-296, 2009.
doi:10.1109/LGRS.2008.2012441 Google Scholar

8. Buiten, H. J. and B. van Putten, "Quality assessment of remote sensing image registration --- Analysis and testing of control point residuals," ISPRS J. Photogramm. Remote Sens., Vol. 52, No. 2, 57-73, 1997.
doi:10.1016/S0924-2716(97)83001-8 Google Scholar

9. Goncalves, H., L. Corte-Real, and J. A. Goncalves, "Automatic image registration through image segmentation and SIFT," IEEE Trans. on Geosci. Remote Sens, Vol. 49, No. 7, 2589-2600, 2011.
doi:10.1109/TGRS.2011.2109389 Google Scholar

10. Gon»calves, H., J. A. Gon»calves, and L. Corte-Real, "Automatic image registration based on correlation and Hough transform," Proc. SPIE Image Signal Process. Remote Sens. XIV, 71090J, 2008.
doi:doi: 10.1117/12.800351 Google Scholar

11. Wang, S., H. You, and K. Fu, "Wang, S., H. You, and K. Fu, BFSIFT: A novel method to find feature matches for SAR image registration," IEEE Geosci. Remote Sens. Lett., Vol. 9, No. 4, 649-653, 2012.
doi:10.1109/LGRS.2011.2177437 Google Scholar

12. Bradley, P. and U. Fayyad, "Refining initial points for K-means clustering," Proc. 15th Int. Conf. Machine Learning, 91-99, 1998. Google Scholar

13. HÄoppner, F., F. Klawonn, and R. Kruse, HÄoppner, F., F. Klawonn, and R. Kruse, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis, and Image Recognition, Wiley, 1999.

14. Hartuv, E. and R. Shamir, "A clustering algorithm based on graph connectivity," Inf. Process. Lett., Vol. 76, No. 4--6 , 6175-6181, 2000. Google Scholar