Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.251404

Keywords:

hyperspectral aerospace images, compression algorithm, discrete transformations, compression ratio, discrete-cosine transformation, Walsh-Hadamard

Abstract

The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table have been developed. The existing solutions to the lossless compression problem for hyperspectral aerospace images are analyzed. Based on them, a compression algorithm is proposed taking into account inter-channel correlation and the Walsh-Hadamard transformation, characterized by data transformation with a decrease in the range of the initial values by forming a set of channel groups [10–15] with high intra-group correlation [0.9–1] of the corresponding pairs with the selection of optimal parameters. The results obtained in the course of the research allow us to determine the optimal parameters for compression: the results of the compression ratio indicators were improved by more than 30 % with an increase in the size of the parameter channels. This is due to the fact that the more values to be converted, the fewer bits are required to store them. The best values of the compression ratio [8–12] are achieved by choosing the number of channels in an ordered group with high correlation.

Supporting Agency

  • This work was carried out within the framework of the IRN research project Grant No. AP09561922 «Development of a mathematical apparatus for the use of hyperspectral images for phytosanitary inspection of grain crops during aerospace survey»

Author Biographies

Assiya Sarinova, S. Seifullin Kazakh Agro Technical University

PhD

Department of Operation of Electrical Equipment

Pavel Dunayev, S. Seifullin Kazakh Agro Technical University

PhD

Department of Radio Engineering, Electronics and Telecommunications

Aigul Bekbayeva, S. Seifullin Kazakh Agro Technical University

MSc, Deputy Director

Center for Technological Competence in the Field of Digitalization of Agro-Industrial Complex

Ali Mekhtiyev, S. Seifullin Kazakh Agro Technical University

PhD, Professor

Department of Operation of Electrical Equipment

Yermek Sarsikeyev, S. Seifullin Kazakh Agro Technical University

PhD, Head of Department

Department of Operation of Electrical Equipment

References

  1. 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. doi: https://doi.org/10.14257/ijsip.2014.7.3.25
  2. Cheng, K.-J., Dill, J. C. (2014). An Improved EZW Hyperspectral Image Compression. Journal of Computer and Communications, 02 (02), 31–36. doi: https://doi.org/10.4236/jcc.2014.22006
  3. 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. doi: https://doi.org/10.17706/ijcee.2014.v6.867
  4. 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://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.429.9325&rep=rep1&type=pdf
  5. 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. Available at: http://ijarece.org/wp-content/uploads/2013/08/IJARECE-VOL-2-ISSUE-3-314-319.pdf
  6. Poonam, Chauhan, R. S. (2013). Compression and Classification of Hyperspectral Images using an Algorithm based on DWT and NTD. Advance in Electronic and Electric Engineering, 3 (4), 447–456. Available at: http://www.ripublication.com/aeee/58_pp%20%20447-456.pdf
  7. Vallakati, M. B., Sedamkar, R. R. (2012). Low Complexity DCT-based DSC approach for Hyperspectral Image Compression with Arithmetic Code. IJCSI International Journal of Computer Science Issues, 9 (5), 277–284. Available at: https://docplayer.net/135044103-Low-complexity-dct-based-dsc-approach-for-hyperspectral-image-compression-with-arithmetic-code.html
  8. Keerthana, P., Sivasankar, A. (2013). The Impact of Lossy Compression on Hyperspectral Data Adaptive Spectral Unmixing and PCA Classification. International Journal of Science and Modern Engineering, 1 (7), 35–37. Available at: https://www.ijisme.org/wp-content/uploads/papers/v1i7/G0340061713.pdf
  9. Mohand, O., Leila, A., Mourad, L., Soltane, A. (2012). Aviris Hyperspectral Images Compression Using 3d Spiht Algorithm. IOSR Journal of Engineering, 02 (10), 31–36. doi: https://doi.org/10.9790/3021-021023136
  10. Aiazzi, B., Alparone, L., Baronti, S., Lastri, C., Selva, M. (2012). Spectral Distortion in Lossy Compression of Hyperspectral Data. Journal of Electrical and Computer Engineering, 2012, 1–8. doi: https://doi.org/10.1155/2012/850637
  11. Liang, Y., Li, J., Guo, K. (2012). Lossless compression of hyperspectral images using hybrid context prediction. Optics Express, 20 (7), 8199. doi: https://doi.org/10.1364/oe.20.008199
  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. doi: https://doi.org/10.1016/j.jvcir.2014.06.008
  13. Pizzolante, R., Carpentieri, B. (2014). Band Clustering for the Lossless Compression of AVIRIS Hyperspectral Images. ACEEE Int. J. on Signal and Image Processing, 5 (1), 1–14. Available at: https://vdocuments.net/204498292-band-clustering-for-the-lossless-compression-of-aviris-hyperspectral.html
  14. Sarinova, A., Zamyatin, A. (2020). Methodology for Developing Algorithms for Compressing Hyperspectral Aerospace Images used on Board Spacecraft. E3S Web of Conferences, 223, 02007. doi: https://doi.org/10.1051/e3sconf/202022302007

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Published

2022-02-25

How to Cite

Sarinova, A., Dunayev, P., Bekbayeva, A., Mekhtiyev, A., & Sarsikeyev, Y. (2022). Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations . Eastern-European Journal of Enterprise Technologies, 1(2(115), 22–30. https://doi.org/10.15587/1729-4061.2022.251404

Issue

Vol. 1 No. 2(115) (2022): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2022 Assiya Sarinova, Pavel Dunayev, Aigul Bekbayeva, Ali Mekhtiyev, Yermek Sarsikeyev

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