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Matching SAR and optical images by independed referencing to a vector map

© 2019 I. A. Kunina, E. I. Panfilova, A. P. Gladkov

Institute for Information Transmission Problems RAS, Moscow, Russia

Received 05 Sep 2018

The paper discusses a method for matching of SAR and optical remote sensing images through their independent visual localization relative to the vector route map. The method is based on a representation of map roads and image ones as a sets of straight line segments approximating the roads. Extraction of such segments from the image is based on the fast Hough transform, while the computation of the transform parameters, connecting the local coordinates of the image with the map coordinates, is performed by matching extracted segments to map ones applying the RANSAC algorithm which uses information about the position and orientation of the segments. The presented results of experiments on real data demonstrate the advantage of this approach in comparison with the method of image alignment not employing vector map.

Key words: remote sensing, optical images, SAR images, image matching, line segment matching, fast Hough transform

DOI: 10.1134/S0235009219010074

Cite: Kunina I. A., Panfilova E. I., Gladkov A. P. Sopostavlenie snimkov v radio i vidimom diapazonakh cherez nezavisimuyu privyazku k vektornoi karte [Matching sar and optical images by independed referencing to a vector map]. Sensornye sistemy [Sensory systems]. 2019. V. 33(1). P. 52-59 (in Russian). doi: 10.1134/S0235009219010074

References:

  • “Psimpl. generic n-dimensional poyline simplification..” http://psimpl.sourceforge.net/. [Online; accessed 03-December-2017].
  • Abulkhanov D., Konovalenko I., Nikolaev D., Savchik A., Shvets E., Sidorchuk D. Neural network-based feature point descriptors for registration of optical and sar images in Tenth International Conference on Machine Vision. 2018. Т. 10696, 106960L.
  • Brady M.L. A fast discrete approximation algorithm for the Radon transform. SIAM Journal on Computing. 1998. Т. 27. № 1. С. 107–119.
  • Cheng H., Zheng S., Yu Q., Tian J., Liu J. Matching of sar images and optical images based on edge feature extracted via svm. 7th International Conference. 2004. Ч. 2. С. 930–933.
  • Errico A., Angelino C.V., Cicala L., Persechino G., Ferrara C., Lega M., Vallario A., Parente C., Masi G., Gaetano R., Scarpa G., Amitrano D., Ruello G., Verdoliva L., Poggi G. Detection of environmental hazards through the feature-based fusion of optical and sar data: a case study in southern italy. International Journal of Remote Sensing. 2015. Т. 36. № 13. С. 3345–3367.
  • Fan B., Huo C., Pan C., Kong Q. Registration of optical and sar satellite images by exploring the spatial relationship of the improved sift. IEEE Geoscience and Remote Sensing Letters. 2013. Т. 10. № 4. С. 657–661.
  • Jähne B. Spatio-temporal image processing: theory and scientific applications. 1993. Т. 751.
  • Kunina I., Terekhin A., Khanipov T., Kuznetsova E., Nikolaev D. Aerial image geolocalization by matching its line structure with route map. Ninth International Conference on Machine Vision. 2017. T. 10341, 103412A.
  • Lopez C.V., Anglberger H., Stilla U. Fusion of very high resolution sar and optical images for the monitoring of urban areas. Urban Remote Sensing Event. 2017. С 1–4.
  • Nikolaev D.P., Karpenko S.M., Nikolaev I.P., Nikolayev P.P. Hough transform: underestimated tool in the computer vision field. Proceedings of the 22th European Conference on Modelling and Simulation. 2008. С. 238–246.
  • Prakash R., Singh D., Pathak N.P. A fusion approach to retrieve soil moisture with sar and optical data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2012. Т. 5. № 1. С. 196–206.
  • SAR and Optical Kunina Dataset. ftp://vis.iitp.ru/opt_sar_-matching_dataset/ (2018).
  • Sidorchuk D., Volkov V., Gladilin S. Perception-oriented fusion of multi-sensor imagery: visible, ir, and sar. Tenth International Conference on Machine Vision. 2018. Т. 10696, 106961I.
  • Socolinsky D.A., Wolff L.B. Multispectral image visualization through first-order fusion. IEEE Transactions on Image Processing. 2002. Т. 11. № 8. С. 923–931.
  • Sui H., Xu C., Liu J., Hua F. Automatic optical-to-sar image registration by iterative line extraction and voronoi integrated spectral point matching. IEEE Transactions on geoscience and remote sensing. 2015. Т. 53. № 11. С. 6058–6072.
  • Ye Y., Shen L., Hao M., Wang J., Xu Z. Robust optical-tosar image matching based on shape properties. IEEE Geoscience and Remote Sensing Letters. 2017. Т. 14. № 4. С. 564–568.
  • Zhang G., Sui H., Song Z., Hua F., Hua L. Automatic registration method of sar and optical image based on line features and spectral graph theory. Multimedia and Image Processing. 2nd International Conference. 2017. С. 64–67.