Development of an algorithm for compressing aerospace images for the subsequent recognition and identification of various objects

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.306973

Keywords:

wavelet transform, Haar wavelet function, compression, hyperspectral aerospace images, compression algorithm, remote sensing

Abstract

The object of study is the recognition and identification of various objects in aerospace images. To solve the problems of compressing hyperspectral aerospace images with losses, the development of a compression algorithm is proposed. As a result, an algorithm has been developed for compressing aerospace images for subsequent recognition and identification of various objects using wavelet transform for processing high- and medium-resolution space images when monitoring from remote sensing satellites, based on the use of structural features of object images. In particular, orthogonal and wavelet transforms are presented, adapted for compression of hyperspectral aerospace images with losses, an adaptive discrete cosine transform algorithm is presented, followed by quantization with a loss level and compression. Thanks to a series of experiments on hyperspectral aerospace images, the effectiveness of the proposed algorithm in terms of the degree of compression, as well as the characteristics of the limits of its applicability, can be highlighted. The use of wavelets provides progressive compression of the bitstream, which makes it possible to achieve lossless compression with minimal loss of information due to the modified Huffman algorithm with a compression ratio of 9 more than 2.5 times in existing algorithms, as well as the quality metric of the restored images, the peak signal-to-noise ratio is sufficiently below 32.56.

The developed compression algorithm demonstrates the effectiveness of its application in terms of a set of characteristics and is superior to analogues. The scope and conditions for the practical use of the results obtained is a comparison of the proposed algorithm with the results of experiments obtained for universal compression algorithms for archivers and a compressor

Author Biographies

Assiya Sarinova, Astana IT University

PhD, Associate Professor

Department of Intelligent Systems and Cybersecurity

Alexandr Neftissov, Astana IT University

PhD, Associate Professor

Research and Innovation Center “Industry 4.0”

Leyla Rzayeva, Astana IT University

PhD, Associate Professor

Department of Intelligent Systems and Cybersecurity

Alimzhan Yessenov, Astana IT University

PhD Candidate, Senior Lecturer

Department of Intelligent Systems and Cybersecurity

Lalita Kirichenko, Astana IT University

Doctoral Student

Research and Innovation Center “Industry 4.0”

Ilyas Kazambayev, Astana IT University

PhD Candidate, Junior Researcher

Research and Innovation Center “Industry 4.0”

References

  1. Mallat, S. (1999). A Wavelet Tour of Signal Processing. Academic Press. https://doi.org/10.1016/b978-0-12-466606-1.x5000-4
  2. Salomon, D. (2007). Data compression: The complete reference. Springer, 1092.
  3. Ceamanos, X., Valero, S. (2016). Processing Hyperspectral Images. Optical Remote Sensing of Land Surface, 163–200. https://doi.org/10.1016/b978-1-78548-102-4.50004-1
  4. Xue, J., Zhao, Y., Liao, W., Chan, J. C.-W. (2019). Hyper-Laplacian regularized nonlocal low-rank matrix recovery for hyperspectral image compressive sensing reconstruction. Information Sciences, 501, 406–420. https://doi.org/10.1016/j.ins.2019.06.012
  5. Prabhakar, T. V. N., Geetha, P. (2017). Two-dimensional empirical wavelet transform based supervised hyperspectral image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 133, 37–45. https://doi.org/10.1016/j.isprsjprs.2017.09.003
  6. Pranitha, K., Kavya, G. (2023). An efficient image compression architecture based on optimized 9/7 wavelet transform with hybrid post processing and entropy encoder module. Microprocessors and Microsystems, 98, 104821. https://doi.org/10.1016/j.micpro.2023.104821
  7. Shi, C., Zhang, J., Zhang, Y. (2016). Content-based onboard compression for remote sensing images. Neurocomputing, 191, 330–340. https://doi.org/10.1016/j.neucom.2016.01.048
  8. Puri, A., Sharifahmadian, E., Latifi, S. (2014). A Comparison of Hyperspectral Image Compression Methods. International Journal of Computer and Electrical Engineering, 6 (6), 493–500. https://doi.org/10.17706/ijcee.2014.v6.867
  9. Lin, H.-C., Hwang, Y.-T. (2011). Lossless Compression of Hyperspectral Images Using Adaptive Prediction and Backward Search Schemes. Journal of Information Science and Engineering, 27, 419–435. Available at: https://www.researchgate.net/publication/220588090_Lossless_Compression_of_Hyperspectral_Images_Using_Adaptive_Prediction_and_Backward_Search_Schemes
  10. Mora Pascual, J., Mora Mora, H., Fuster Guilló, A., Azorín López, J. (2015). Adjustable compression method for still JPEG images. Signal Processing: Image Communication, 32, 16–32. https://doi.org/10.1016/j.image.2015.01.004
  11. KazEOSat-1. Available at: https://www.eoportal.org/satellite-missions/kazeosat-1
  12. Nian, Y., He, M., Wan, J. (2015). Lossless and near-lossless compression of hyperspectral images based on distributed source coding. Journal of Visual Communication and Image Representation, 28, 113–119. https://doi.org/10.1016/j.jvcir.2014.06.008
  13. Cheng, K., Dill, J. (2013). Hyperspectral images lossless compression using the 3D binary EZW algorithm. Image Processing: Algorithms and Systems XI. https://doi.org/10.1117/12.2002820
  14. Li, C., Guo, K. (2014). Lossless Compression of Hyperspectral Images Using Three-Stage Prediction with Adaptive Search Threshold. International Journal of Signal Processing, Image Processing and Pattern Recognition, 7 (3), 305–316. https://doi.org/10.14257/ijsip.2014.7.3.25
  15. Gashnikov, M., Glumov, N. (2016). Onboard processing of hyperspectral data in the remote sensing systems based on hierarchical compression. Computer Optics, 40 (4), 543–551. https://doi.org/10.18287/2412-6179-2016-40-4-543-551
  16. Gashnikov, M. (2017). Minimizing the entropy of post-interpolation residuals for image compression based on hierarchical grid interpolation. Computer Optics, 41 (2), 266–275. https://doi.org/10.18287/2412-6179-2017-41-2-266-275
  17. Sarinova, A., Zamyatin, A., Cabral, P. (2015). Lossless compression of hyperspectral images with pre-byte processing and intra-bands correlation. DYNA, 82 (190), 166–172. https://doi.org/10.15446/dyna.v82n190.43723
  18. Zamjatin, A. V., Sarinova, A. Zh. (2017). Algorithm for compressing hyperspectral aerospace images using mathematical processing and taking into account interband correlation. Materials of the IV International Scientific Conference "Regional Problems of Earth Remote Sensing", 157–160.
  19. Dubey, V. A., Dubey, R. (2013). A New Set Partitioning in Hierarchical (SPIHT) Algorithm and Analysis with Wavelet Filters. International Journal of Innovative Technology and Exploring Engineering, 3 (3), 125–128. Available at: https://www.ijitee.org/wp-content/uploads/papers/v3i3/C1132083313.pdf
  20. Kiely, A., Klimesh, M., Xie, H., Aranki, N. (2006). ICER-3D: A Progressive Wavelet-Based Compressor for Hyperspectral Images. The Interplanetary Network Progress Report.
  21. Sindhuja, N. M., Arumugam, A. S. (2013). SPIHT based compression of hyper spectral images. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2 (10), 4933–4938. Available at: https://www.ijareeie.com/upload/2013/october/26SPIHT.pdf
  22. Penna, B., Tillo, T., Magli, E., Olmo, G. (2006). Progressive 3-D Coding of Hyperspectral Images Based on JPEG 2000. IEEE Geoscience and Remote Sensing Letters, 3 (1), 125–129. https://doi.org/10.1109/lgrs.2005.859942
  23. Sujithra, D. S., Manickam, T., Sudheer, D. S. (2013). Compression of hyperspectral image using discrete wavelet transform and Walsh Hadamard transform. International journal of advanced research in electronics and communication engineering (IJARECE), 2 (3), 314–319.
  24. Pizzolante, R., Carpentieri, B. (2013). On the Compression of Hyperspectral Data. IT CoNvergence PRActice (INPRA), 1 (4), 24–38. Available at: https://isyou.info/inpra/papers/inpra-v1n4-02.pdf
  25. Christophe, E. (2011). Hyperspectral Data Compression Tradeoff. Optical Remote Sensing, 9–29. https://doi.org/10.1007/978-3-642-14212-3_2
Development of an algorithm for compressing aerospace images for the subsequent recognition and identification of various objects

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Published

2024-06-28

How to Cite

Sarinova, A., Neftissov, A., Rzayeva, L., Yessenov, A., Kirichenko, L., & Kazambayev, I. (2024). Development of an algorithm for compressing aerospace images for the subsequent recognition and identification of various objects. Eastern-European Journal of Enterprise Technologies, 3(2 (129), 83–94. https://doi.org/10.15587/1729-4061.2024.306973

Issue

Vol. 3 No. 2 (129) (2024): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2024 Assiya Sarinova, Alexandr Neftissov, Leyla Rzayeva, Alimzhan Yessenov, Lalita Kirichenko, Ilyas Kazambayev

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