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Joint Source-and-Channel Coding for Small Satellite Applications (2403.06802v1)

Published 11 Mar 2024 in cs.NI

Abstract: Small satellites are widely used today as cost effective means to perform Earth observation and other tasks that generate large amounts of high-dimensional data, such as multi-spectral imagery. These satellites typically operate in low earth orbit, which poses significant challenges for data transmission due to short contact times with ground stations, low bandwidth, and high packet loss probabilities. In this paper, we introduce JSCC-Sat, which applies joint source-and-channel coding using neural networks to provide efficient and robust transmission of compressed image data for satellite applications. We evaluate our mechanism against traditional transmission schemes with separate source and channel coding and demonstrate that it outperforms the existing approaches when applied to Earth observation data of the Sentinel-2 mission.

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References (35)
  1. J. Wang, D. Li, W. Cao, X. Lou, A. Shi, and H. Zhang, “Remote sensing analysis of erosion in Arctic coastal areas of Alaska and eastern Siberia,” Remote Sensing, vol. 14, no. 3, 2022.
  2. “Radix,” accessed: 2023-04-28. [Online]. Available: https://space.skyrocket.de/doc_sdat/radix.htm
  3. P. Barmpoutis, P. Papaioannou, K. Dimitropoulos, and N. Grammalidis, “A review on early forest fire detection systems using optical remote sensing,” Sensors, vol. 20, no. 22, 2020.
  4. “Marco,” accessed: 2023-04-28. [Online]. Available: https://www.jpl.nasa.gov/missions/mars-cube-one-marco
  5. C. Nogales, B. Grim, M. Kamstra, B. Campbell, A. Ewing, R. Hance, J. Griffin, and S. Parke, “MakerSat-0: 3D-printed polymer degradation first data from orbit,” 08 2018.
  6. European Space Agency, “Sentinel-2 user handbook,” 2015. [Online]. Available: https://sentinel.esa.int/documents/247904/685211/sentinel-2_user_handbook
  7. V. Kostina and S. Verdú, “Lossy joint source-channel coding in the finite blocklength regime,” IEEE Transactions on Information Theory, vol. 59, no. 5, pp. 2545–2575, 2013.
  8. W. Yu, Z. Sahinoglu, and A. Vetro, “Energy efficient jpeg 2000 image transmission over wireless sensor networks,” in IEEE Global Telecommunications Conference, 2004. GLOBECOM ’04., vol. 5, 2004, pp. 2738–2743 Vol.5.
  9. S. Appadwedula, D. Jones, K. Ramchandran, and L. Qian, “Joint source channel matching for a wireless image transmission,” in Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), vol. 2, 1998, pp. 137–141 vol.2.
  10. J. Cai and C. W. Chen, “Robust joint source-channel coding for image transmission over wireless channels,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 10, no. 6, pp. 962–966, 2000.
  11. T.-Y. Tung, D. B. Kurka, M. Jankowski, and D. Gündüz, “Deepjscc-q: Channel input constrained deep joint source-channel coding,” in ICC 2022 - IEEE International Conference on Communications, 2022, pp. 3880–3885.
  12. E. Bourtsoulatze, D. B. Kurka, and D. Gunduz, “Deep joint source-channel coding for wireless image transmission,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 567–579, sep 2019.
  13. Y. Hu, W. Yang, Z. Ma, and J. Liu, “Learning end-to-end lossy image compression: A benchmark,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 8, pp. 4194–4211, 2022.
  14. M. Yang and H.-S. Kim, “Deep joint source-channel coding for wireless image transmission with adaptive rate control,” 2021.
  15. D. B. Kurka and D. Gündüz, “Bandwidth-agile image transmission with deep joint source-channel coding,” IEEE Transactions on Wireless Communications, vol. 20, no. 12, pp. 8081–8095, 2021.
  16. O. Y. Bursalioglu, G. Caire, and D. Divsalar, “Joint source-channel coding for deep space image transmission using rateless codes,” in 2011 Information Theory and Applications Workshop, 2011, pp. 1–10.
  17. F. A. Aoudia and J. Hoydis, “Model-free training of end-to-end communication systems,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 11, pp. 2503–2516, 2019.
  18. M. Yang, C. Bian, and H.-S. Kim, “Deep joint source channel coding for wirelessimage transmission with ofdm,” 2021.
  19. Z. Xuan and K. Narayanan, “Deep joint source-channel coding for transmission of correlated sources over awgn channels,” in ICC 2021 - IEEE International Conference on Communications, 2021, pp. 1–6.
  20. F. Kong, K. Hu, Y. Li, D. Li, and S. Zhao, “Spectral–spatial feature partitioned extraction based on CNN for multispectral image compression,” Remote Sensing, vol. 13, no. 1, p. 9, Dec. 2020.
  21. V. A. de Oliveira, M. Chabert, T. Oberlin, C. Poulliat, M. Bruno, C. Latry, M. Carlavan, S. Henrot, F. Falzon, and R. Camarero, “Reduced-complexity end-to-end variational autoencoder for on board satellite image compression,” Remote Sensing, vol. 13, no. 3, p. 447, Jan. 2021.
  22. V. Alves de Oliveira, M. Chabert, T. Oberlin, C. Poulliat, M. Bruno, C. Latry, M. Carlavan, S. Henrot, F. Falzon, and R. Camarero, “Satellite image compression and denoising with neural networks,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
  23. F. Kong, S. Zhao, Y. Li, D. Li, and Y. Zhou, “A residual network framework based on weighted feature channels for multispectral image compression,” Ad Hoc Networks, vol. 107, p. 102272, Oct. 2020.
  24. “sentinel2,” accessed: 2023-04-28. [Online]. Available: https://sentinel.esa.int/web/sentinel/missions/sentinel-2
  25. ESA, “Vojvodina, Serbia,” 2016. [Online]. Available: https://www.esa.int/ESA_Multimedia/Images/2017/04/Vojvodina_Serbia
  26. A. P. Arechiga, A. J. Michaels, and J. T. Black, “Onboard image processing for small satellites,” in NAECON 2018 - IEEE National Aerospace and Electronics Conference, 2018, pp. 234–240.
  27. S. Agarwal, E. Hervas-Martin, J. Byrne, A. Dunne, J. Luis Espinosa-Aranda, and D. Rijlaarsdam, “An evaluation of low-cost vision processors for efficient star identification,” Sensors, vol. 20, no. 21, 2020. [Online]. Available: https://www.mdpi.com/1424-8220/20/21/6250
  28. E. Dunkel, J. Swope, Z. Towfic, S. Chien, D. Russell, J. Sauvageau, D. Sheldon, J. Romero-Cañas, J. L. Espinosa-Aranda, L. Buckley, E. Hervas-Martin, M. Fernandez, and C. Knox, “Benchmarking deep learning inference of remote sensing imagery on the qualcomm snapdragon and intel movidius myriad x processors onboard the international space station,” in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 5301–5304.
  29. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Y. Bengio and Y. LeCun, Eds., 2015. [Online]. Available: http://arxiv.org/abs/1409.1556
  30. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
  31. G. Giuffrida, L. Fanucci, G. Meoni, M. Batič, L. Buckley, A. Dunne, C. van Dijk, M. Esposito, J. Hefele, N. Vercruyssen, G. Furano, M. Pastena, and J. Aschbacher, “The ϕitalic-ϕ\phiitalic_ϕ-sat-1 mission: The first on-board deep neural network demonstrator for satellite earth observation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2022.
  32. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv preprint 1512.03385, 2015.
  33. O. Popescu, J. S. Harris, and D. C. Popescu, “Designing the communication sub-system for nanosatellite cubesat missions: Operational and implementation perspectives,” in SoutheastCon 2016, 2016, pp. 1–5.
  34. G. Sumbul, M. Charfuelan, B. Demir, and V. Markl, “BigEarthNet: A large-scale benchmark archive for remote sensing image understanding,” in IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 5901–5904.
  35. G. Sumbul, A. de Wall, T. Kreuziger, F. Marcelino, H. Costa, P. Benevides, M. Caetano, B. Demir, and V. Markl, “BigEarthNet-MM: A large-scale, multimodal, multilabel benchmark archive for remote sensing image classification and retrieval [software and data sets],” IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 3, pp. 174–180, Sep. 2021.
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