Geometrical accuracy of remote sensing data often is ensured by geometrical transforms based on Ground Control Points (GCPs). Manual selection of GCP is a time-consuming process, which requires some sort of automation. Therefore, the aim of this study is to present and evaluate methodology for easier, semi-automatic selection of ground control points for urban areas. Custom line scanning algorithm was implemented and applied to data in order to extract potential GCPs for an image analyst. The proposed method was tested for classical orthorectification and special object polygon transform. Results are convincing and show that in the test case semi-automatic methodology is able to correct locations of 70 % (thermal data) – 80 % (orthophoto images) of buildings. Geometrical transform for subimages of approximately 3 hectares with approximately 12 automatically found GCPs resulted in RSME approximately 1 meter with standard deviation of 1.2 meters.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 P. M. Mather, Computer Processing of Remotely-Sensed Images: an introduction, 3rd ed. John Wiley & Sons, 2005, 324 p.
 J. Wang, Y. Ge, G. B. M. Heuvelink, C. Zhou and D. Brus, “Effect of the sampling design of ground control points on the geometric correction of remotely sensed imagery,” International Journal of Applied Earth Observation and Geoinformation, vol. 18, pp. 91–100, Aug. 2012. https://doi.org/10.1016/j.jag.2012.01.001
 W. Ma, J. Yang, X. Ning and W. Gao, “A quantitative evaluation method of ground control points for remote sensing image registration,” Progress In Electromagnetics Research M, vol. 34, pp. 55–62, 2014. https://doi.org/10.2528/PIERM13092902
 O. Benarchid and N. Raissouni, “Support Vector Machines for Object Based Building Extraction in Suburban Area using Very High Resolution Satellite Images, a Case Study: Tetuan, Morocco,” IAES International Journal of Artificial Intelligence, vol. 2, iss. 1, March 2013. https://doi.org/10.11591/ij-ai.v2i1.1781
 Y. K. Han, Y. G. Byun, J. W. Choi, D. Y. Han and Y. I. Kim, “Automatic registration of high-resolution images using local properties of features,” Photogrammetric engineering & remote sensing, vol. 78 no. 3, pp. 211–221, March 2012. https://doi.org/10.14358/PERS.78.3.211
 C. C. Liu and P. L. Chen, “Automatic extraction of ground control regions and orthorectification of remote sensing imagery,” Optics express, vol. 17, iss. 10, pp. 7970–7984, 2009. https://doi.org/10.1364/OE.17.007970
 L. J. Quackenbush, “A review of techniques for extracting linear features from imagery,” Photogrammetric Engineering & Remote Sensing, vol. 70, no. 12, pp. 1383–1392, Dec. 2004. https://doi.org/10.14358/PERS.70.12.1383
 W. Yang, X. Wang, B. Moran, A. Wheaton and N. Cooley, “Efficient registration of optical and infrared images via modified Sobel edging for plant canopy temperature estimation,” Computers & Electrical Engineering, vol. 38, iss. 5, pp. 1213–1221, Sep. 2012. https://doi.org/10.1016/j.compeleceng.2012.05.014
 J. Yao, M. R. Ruggeri, P. Taddei and V. Sequeira, “Automatic scan registration using 3D linear and planar features,” 3D Research, vol. 1, iss. 6, pp. 1–18, 2010. https://doi.org/10.1007/3DRes.03(2010)06
 Y. Ye and J. Shan, “A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 90, pp. 83–95, Apr. 2014. https://doi.org/10.1016/j.isprsjprs.2014.01.009
 L. Yu, D. Zhang and E. J. Holden, “A fast and fully automatic registration approach based on point features for multi-source remote-sensing images,” Computers & Geosciences, vol. 34, iss. 7, pp. 838–848, July 2008. https://doi.org/10.1016/j.cageo.2007.10.005
 A. Sedaghat, M. Mokhtarzade and H. Ebadi, “Uniform robust scale-invariant feature matching for optical remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, iss. 11, pp. 4516–4527, Nov. 2011. https://doi.org/10.1109/TGRS.2011.2144607
 S. Khorram, “A feature-based image registration algorithm using improved chain-code representation combined with invariant moments,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, iss. 5, pp. 2351–2362, Sep. 1999. https://doi.org/10.1109/36.789634
 H. Sui, C. Xu, J. Liu and F. Hua, “Automatic Optical-to-SAR Image Registration by Iterative Line Extraction and Voronoi Integrated Spectral Point Matching,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, iss. 11, pp. 6058–6072, Nov. 2015. https://doi.org/10.1109/TGRS.2015.2431498
 Z. Xiong and Y. Zhang, “A novel interest-point-matching algorithm for high-resolution satellite images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, iss. 12, pp. 4189–4200, Dec. 2009. https://doi.org/10.1109/TGRS.2009.2023794
 R. C. Gonzalez and R. E. Woods, Digital image processing, 2008.
 Mathworks. Find edges in intensity image. User Manual, 2016.
 N. Aggarwal and W. C. Karl, “Line detection in images through regularized Hough transform,” IEEE transactions on image processing, vol. 15, iss. 3, pp. 582–591, March 2006. https://doi.org/10.1109/TIP.2005.863021