Estimation of mutual subpixel shift between satellite images: software implementation

Keywords: satellite imagery, subpixel shift, software implementation

Abstract

The special-purpose software implementation for estimating the subpixel shift between satellite images using advanced computer technology is described in this paper. The automatic calculation of the mutual subpixel shift between a pair of digital satellite images by correlation algorithm is performed. The proposed implementation was tested on a statistically representative number of satellite images and reached acceptable accuracy in determining their subpixel shift values.

References

Aydin, S. (2015). Software competences of geomatic engineering. International Journal of Geosciences, 6 (12), 62118.

Boreman, S. & Stevenson, R. (1998). Spatial resolution enhancement of low-resolution image sequences: A comprehensive review with directions for future research. Laboratory for Image and Signal Analysis (LISA) Technical Report. Notre Dame: University of Notre Dame.

Butyrin, S.A. (2015). A method for transformation of the space photos obtained at the distributed scanning optoelectronic observations. Bulletin of Samara Scientific Center of the RAS, 17 (6), 702-706. (in Russian).

d’Angelo, P. (2013). Automatic orientation of large multitemporal satellite image blocks. Proceedings of International Symposium on Satellite Mapping Technology and Application (ISSMTA2013), 1-7. Nanjing: ISPRS.

Dawn, S., Saxena, V. & Sharma, B. (2010). Remote sensing image registration techniques: A survey. Proceedings of the 4th International Conference on Image and Signal Processing, (ICISP 2010), 103-112. Québec: Springer. https://doi.org/10.1007/978-3-642-13681-8_13

Ferraris, V., Dobigeon, N., Wei, Q. & Chabert, M. (2018). Detecting changes between optical images of different spatial and spectral resolutions: A fusion-based approach. IEEE Transactions on Geoscience and Remote Sensing, 56 (3), 1566-1578. https://doi.org/10.1109/tgrs.2017.2765348

Fetisov, D.V., Kolesenkov, A.N., Babaev, S.I. & Fetisova, T.A. (2019). Development of a model for subpixel processing of aerospace images during remote sensing of the Earth. Science Bulletin of the NSTU, 2 (75), 89-100. (in Russian). https://doi.org/10.17212/1814-1196-2019-2-89-100

Hong, A.-N. & Woo, D.-M. (2014). Fast stereo matching of high resolution satellite images using a new tilting technique. Proceedings of the 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET’2014), 91-95. London: IIE. https://doi.org/10.15242/iie.e0514561

Kwan, C. (2018). Image resolution enhancement for remote sensing applications. Proceedings of the 2nd International Conference on Vision, Image and Signal Processing, 12. Las Vegas: ACM. https://doi.org/10.1145/3271553.3271590

Milanfar, P. (Ed.). (2010). Super-Resolution Imaging. Boca Raton: CRC Press.

Moigne, J.L., Netanyahu, N.S. & Eastman, R.D. (Eds). (2011). Image Registration for Remote Sensing. Cambridge: Cambridge University Press.

Popov, M.A., Stankevich, S.A. & Shklyar, S.V. (2015). An algorithm for resolution enhancement of subpixel displaced images. Mathematical Machines and Systems, 1, 29-36. (in Russian).

Reddy, B.S. & Chatterji, B.N. (1996). An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Transactions on Image Processing, 5 (8), 1266-1271. https://doi.org/10.1109/83.506761

Stankevich, S.A. (1994). The models for segmented discrete images automatic matching. Proceedings of the 2nd All-Ukrainian International Conference on Signal/Image Processing and Pattern Recognition, 167-169. Kiev: Institute of Cybernetics NAS of Ukraine. (in Ukrainian).

Stankevich, S.A., Shklyar, S.V. & Lubskyi, N.S. (2013). Aerial imaging spatial resolution enhancement based on subpixel image registration. Proceedings of Aviation Research Institute, 9 (16), 125-132. (in Ukrainian).

Stankevich, S.A., Shklyar, S.V. & Tyagur, V.M. (2013). Satellite imagery resolution enhancement using subpixel frames acquisition. Journal of Information, Control and Management Systems, 11 (2), 135-144.

Vandewalle, P., Süsstrunk, S. & Vetterli, M. (2003). Superresolution images reconstructed from aliased images. Proceedings of the SPIE, 5150, 1398-1405. https://doi.org/10.1117/12.506874

Voronin, E.G. (2017). On the displacements of the contours of the optic-electronic space images. Causes and evaluation of offsets. Geodesy and Cartography, 78 (5), 34-41. (in Russian). https://doi.org/10.22389/0016-7126-2017-923-5-34-41

Young, S.S., Driggers, R.G. & Jacobs, E.L. (2008). Signal Processing and Performance Analysis for Imaging Systems. Norwood: Artech House.

Zhu, L., Erving, A., Koistinen, K., Nuikka, M., Junnilainen, H., Heiska, N. & Haggrén, H. (2008). Georeferencing multi-temporal and multi-scale imagery in photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII (B5), 225-230.

Section
Techniques for Earth observation data acquisition, processing and interpretation