Sequential Modelling of Remote Earth Sensing Process

Authors

DOI:

https://doi.org/10.18486/ijcsnt/13.1.172

Keywords:

Remote Earth Sensing (RES), Sequential Modelling, Electromagnetic Wave Propagation, Micro-Satellites, Linear Image Sensors, Filter-Based Modelling, Satellite Imaging, Beam Refraction, Earth Observation

Abstract

This paper proposes a method of modelling of remote Earth sensing (RES) process, based on modelling of electromagnetic beam passing through various layers located in between Earth surface and registering sensor, like a number of filters. Such approach allows performing a flexible investigation of RES process, along with providing an ability for scientists to work on models of different layers in parallel in terms of the protocol, defined for inter-filter data flow exchange.

References

A. Demin, N. Kropotskiy “Imitiation modelling of measurement and control systems” ITMO University Saint-Petersburg, 2007.

A. Demin, A. Denisov, I. Perl, A. Tretiakova “ Opto-electronic complex цшер increased productivity”, ITMO Herald, issue 3(73). Saint-Petersburg, 2011.

G. Avanesov, A. Vasilevskiy, Y. Ziman, I. Polanskiy “ Digital aerial filming systems of linear CCD detectors” in “Current problems in remote sensing of the Earth from Space”, vol. 2, CPRSES, Moskow, 2005, pp. 189–195.

A. Kucheyko, “Artificial satellites. Mini Satellite for operational intelligence” in “News of Cosmonautics”, vol 2, Moskow, 2006, pp 50-51

Kang, E. L., Cressie, N. and Shi, T. (2010) Using temporal variability to improve spatial mapping with application to satellite data. Canadian Journal of Statistics 38, 271–89. DOI: https://doi.org/10.1002/cjs.10063

Gao, J.; Yuan, Q.; Li, J.; Zhang, H.; Su, X. Cloud removal with fusion of high resolution optical and SAR images using generative adversarial networks. Remote Sens. 2020, 12, 191. DOI: https://doi.org/10.3390/rs12010191

T. Chen, Z. Lu, Y. Yang, Y. Zhang, B. Du, and A. Plaza, “A Siamese network based U-Net for change detection in high resolution remote sensing images,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 15, pp. 2357–2369, 2022. DOI: https://doi.org/10.1109/JSTARS.2022.3157648

R. C. Daudt, B. Le Saux, and A. Boulch, “Fully convolutional siamese networks for change detection,” in Proc. 25th IEEE Int. Conf. Image Process., IEEE, 2018, pp. 4063–4067. DOI: https://doi.org/10.1109/ICIP.2018.8451652

J. Ma, B. Li, H. Li, S. Meng, R. Lu and S. Mei, "Remote Sensing Change Detection by Pyramid Sequential Processing With Mamba," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 19481-19495, 2025, doi: 10.1109/JSTARS.2025.3591834. DOI: https://doi.org/10.1109/JSTARS.2025.3591834

Shi, T. and Cressie, N. (2007) Global statistical analysis of MISR aerosol data: a massive data product from NASA's Terra satellite. Environmetrics 18, 665–80. DOI: https://doi.org/10.1002/env.864

R.J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, "Image change detection algorithms: a systematic survey", IEEE Transactions on Image Processing, Volume 14, Issue 3, March 2005, pp. 294 – 307. DOI: https://doi.org/10.1109/TIP.2004.838698

Downloads

Published

2024-04-30

How to Cite

Sequential Modelling of Remote Earth Sensing Process. (2024). International Journal of Communication Systems and Network Technologies, 13(1), 21-28. https://doi.org/10.18486/ijcsnt/13.1.172