Visually lossless compression of multilook SAR images

Vladimir Lukin, Sergii Kryvenko, Andrii Pavliuk

Abstract


Synthetic aperture radars (SARs) produce a large amount of remote sensing data useful for numerous applications. SAR image resolution improves, leading to an increased size of acquired images that must be transferred to on-land data processing centers or directly to customers. Then, SAR data compression is required. Nowadays, lossy compression is mostly applied, but it is necessary to control losses to avoid undesired (inappropriate) deterioration of useful information contained in SAR images. In this study, we consider visually lossless compression of multilook SAR images using several lossy compression techniques (including modern coders such as BPG, AVIF, and HEIF) and both conventional and visual quality metrics (including PSNR-HVS-M, HaarPSI, and MS-SSIM). Such metrics and the corresponding distortion invisibility thresholds are employed to achieve the maximum possible compression ratio. We show that, in general, the attained compression ratios are approximately 3 if calculated for 8-bit representation of the images to be compressed. Depending on the visual quality metric used, different coders might produce the largest compression ratio. The considered compression techniques are directly applied to normalized SAR without preliminary variance stabilizing transforms. The images used in the tests have the same speckle characteristics as the real-life images acquired by Sentinel-1 synthetic aperture radar operating in multilook mode, i.e., the speckle is simulated as quasi-Gaussian multiplicative spatially correlated noise. Examples of original and compressed images are presented, demonstrating their very high similarity and practical invisibility of distortions introduced by lossy compression. The obtained results are discussed, and further research directions are proposed. In particular, the use of variance-stabilizing transforms must be considered.

Keywords


synthetic aperture radar; visually lossless compression; speckled image

Full Text:

PDF

References


Varade, D., Singh, G., Dikshit, O., & Manickam, S. Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy. Water Resources Research, 2020, vol. 56, pp. 1–18. DOI: 10.1029/2019WR025449.

Lysenko, A. SAR Data Spatial Resolution Enhancement for Environmental Monitoring Tasks. International Conference of Young Professionals «GeoTerrace-2023», Oct 2023, Volume 2023, iss. 1, pp. 1–5. DOI: 10.3997/2214-4609.2023510010.

Asiyabi, R. M., Ghorbanian, A., Tameh, S. N., Amani, M., Jin, S., & Mohammadzadeh, A. Synthetic Aperture Radar (SAR) for Ocean: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, vol. 16, pp. 9106–9138. DOI. 10.1109/JSTARS.2023.3310363.

Gao, W., Liu, Y., Zeng, Y., Liu, Q., & Li, Q. (2023). SAR Image Ship Target Detection Adversarial Attack and Defence Generalization Research. Sensors, 2023, vol. 23, iss. 4. DOI: 10.3390/s23042266.

Okada, Y., Nakamura, S., Iribe, K., Yokota, Y., Tsuji, M., Tsuchida, M., Hariu, K., Kankaku, Y., Suzuki, S., Osawa, Y., & Shimada, M. System design of wide swath, high resolution, full polarimietoric L-band SAR onboard ALOS-2. 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013, pp. 2408–2411. DOI: 10.1109/IGARSS.2013.6723305.

Breit, H., Wiehle, S., Mandapati, S., Günzel, D., & Balss, U. Demonstrating a SAR Satellite Onboard Processing Chain. 7th Workshop on RF and Microwave Systems, Instruments & Sub-systems + 5th Ka-band Workshop, 2022, pp. 1-6. Available at: https://elib.dlr.de/188283/1/SO6P1P_DLR_Demonstrating_a_SAR_Satellite_Onboard_Processing-Breit-Helko.pdf (accessed 12.6.2024).

Datcu, M., Schwarz, G., Schmidt, K., & Reck, C. Quality Evaluation of Compressed Optical and SAR Images: JPEG vs. Wavelets. Proc. of 1995 International Geoscience and Remote Sensing Symposium, IGARSS ’95, Florence, Italy, 10-14 July 1995, pp. 1687–1689. Available at: https://elib.dlr.de/23745/(accessed 12.6.2024).

Kozhemiakin, R., Abramov, S., Lukin, V., Djurović, B., Djurović, I., & Simeunović, M. Strategies of SAR image lossy compression by JPEG2000 and SPIHT. 2017 6th Mediterranean Conference on Embedded Computing (MECO), 2017, pp. 1–6. DOI: 10.1109/MECO.2017.7977176.

Deng, J., & Huang, L. Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance. Remote Sensing, 2024, vol. 16, iss. 5, article on. 891. DOI: 10.3390/rs16050891.

Rusyn, B., Lutsyk, O., Lysak, Y., Lukenyuk, A., & Pohreliuk, L. Lossless image compression in the remote sensing applications. 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 2016, pp. 195–198. DOI: 10.1109/DSMP.2016.7583539.

Yin, D., Gu, Z., Zhang, Y., Gu, F., Nie, S., Feng, S., Ma, J., & Yuan, C. Speckle noise reduction in coherent imaging based on deep learning without clean data. Optics and Lasers in Engineering, 2020, vol. 133, article no. 106151. DOI: 10.1016/j.optlaseng.2020.106151.

Ko, J., & Lee, S. SAR Image Despeckling Using Continuous Attention Module. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, vol. 15, pp. 3–19. DOI: 10.1109/JSTARS.2021.3132027.

Sun, Z., Leng, X., Zhang, M., Ren, H., & Ji, K. SAR Image Object Detection and Information Extraction: Methods and Applications. Remote Sensing, 2025, vol. 17, iss. 12, article no. 2098. DOI: 10.3390/rs17122098.

Liu, Z., Wang, S., & Gu, Y. SAR Image Compression with Inherent Denoising Capability Through Knowledge Distillation. IEEE Geoscience and Remote Sensing Letters, 2024, vol. 21, article no. 4008005, pp. 1–5. DOI: 10.1109/LGRS.2024.3386758.

Ponomarenko, N. N., Lukin, V. V., Kozhemiakin, R. A., Egiazarian, K. O., & Chobanu, M. K. Lossy and visually lossless compression of single-look SAR images. Telecommunications and Radio Engineering, 2013, vol. 72, iss. 8, pp. 711–729. DOI: 10.1615/TelecomRadEng.v72.i8.60.

Kryvenko, S., Lukin, V., & Vozel, B. Lossy Compression of Single-channel Noisy Images by Modern Coders. Remote Sensing, 2024, vol. 16, iss. 12, article no. 2093. DOI: 10.3390/rs16122093.

Odegard, J. E., Guo, H., Lang, M., Burrus, C. S., Jr., R. O. W., Novak, L. M., & Hiett, M. Wavelet-based SAR speckle reduction and image compression. In Algorithms for Synthetic Aperture Radar Imagery II, 1995, vol. 2487, pp. 259–271. SPIE. DOI: 10.1117/12.210843.

Bondzulic, B., Pavlović, B., Stojanovic, N., & Petrovic, V. Picture-wise just noticeable difference prediction model for JPEG image quality assessment. Military Technical Courier, 2022, vol. 70, pp. 62–86. DOI: 10.5937/vojtehg70-34739.

Yee, D., Soltaninejad, S., Hazarika, D., Mbuyi, G., Barnwal, R., & Basu, A. Medical image compression based on region of interest using better portable graphics (BPG). 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, 2017, pp. 216–221. DOI: 10.1109/SMC.2017.8122605.

Lainema, J., Hannuksela, M., Malamal Vadakital, V., & Aksu, E. HEVC still image coding and high efficiency image file format. 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 2016, pp. 71-75. DOI: 10.1109/ICIP.2016.7532321.

Chen, Y., Mukherjee, D., Han, J., Grange, A., Xu, Y., Parker, S., Chen, C., Su, H., Joshi, U., Chiang, C.-H., Wang, Y., Wilkins, P., Bankoski, J., Trudeau, L., Egge, N., Valin, J.-M., Davies, T., Midtskogen, S., Norkin, A., & Liu, Z. An Overview of Coding Tools in AV1: the First Video Codec from the Alliance for Open Media. APSIPA Transactions on Signal and Information Processing, 2020, vol. 9, iss. 1, article no. e6. DOI: 10.1017/ATSIP.2020.2.

Testolina, M., Lazzarotto, D., Rodrigues, R., Mohammadi, S., Ascenso, J., Pinheiro, A., & Ebrahimi, T. On the Performance of Subjective Visual Quality Assessment Protocols for Nearly Visually Lossless Image Compression. MM '23: Proceedings of the 31st ACM International Conference on Multimedia, 2023, pp. 6715–6723. DOI: 10.1145/3581783.3613835.

Jamil, S. Review of Image Quality Assessment Methods for Compressed Images. Journal of Imaging, 2024, vol. 10, iss. 5, article no. 113. DOI: 10.3390/jimaging10050113.

Nafchi, H., Shahkolaei, A., Hedjam, R., & Cheriet, M. Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator. IEEE Access, 2016, vol. 4, pp. 5579–5590. DOI: 10.1109/ACCESS.2016.2604042.

Reisenhofer, R., Bosse, S., Kutyniok, G., & Wiegand, T. A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment. Signal Processing Image Communication, 2018, vol. 61, pp. 33–43. DOI: 10.1016/j.image.2017.11.001.

Li, F., Ieremeiev, O., Lukin, V., & Egiazarian, K. BPG-Based Lossy Compression of Three-Channel Remote Sensing Images with Visual Quality Control. Remote Sensing, 2024, vol. 16, iss. 15, article no. 2740. DOI: 10.3390/rs16152740.

Sentinel-1 - Sentinel Online. Available at: https://sentinels.copernicus.eu/copernicus/sentinel-1. (accessed 12.6.2024).

Abdikan, S., Balik Sanli, F., Ustuner, M., & Calò, F. Land Cover Mapping Using SENTINEL-1 SAR Data. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B7, 2016, pp. 757–761. DOI: 10.5194/isprs-archives-XLI-B7-757-2016.

Fan, D., Zhao, T., Jiang, X., García-García, A., Schmidt, T., Samaniego, L., Attinger, S., Wu, H., Jiang, Y., Shi, J., Fan, L., Tang, B.-H., Wagner, W., Dorigo, W., Gruber, A., Mattia, F., Balenzano, A., Brocca, L., Jagdhuber, T., …, & Peng, J. A Sentinel-1 SAR-based global 1-km resolution soil moisture data product: Algorithm and preliminary assessment. Remote Sensing of Environment, 2025, vol. 318, article no. 114579. DOI: 10.1016/j.rse.2024.114579.

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, vol. 13, iss. 4, pp. 600–612. DOI: 10.1109/TIP.2003.819861.

Rubel, O., Lukin, V., Rubel, A., & Egiazarian, K. Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images. Remote Sensing, 2021, vol. 13, iss. 10. DOI: 10.3390/rs13101887.

Di Martino, G., Poderico, M., Poggi, G., Riccio, D., & Verdoliva, L. SAR image simulation for the assessment of despeckling techniques. 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012, pp. 1797–1800. DOI: 10.1109/IGARSS.2012.6351163.

De Fioravante, P., Luti, T., Cavalli, A., Giuliani, C., Dichicco, P., Marchetti, M., Chirici, G., Congedo, L., & Munafò, M. (2021). Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification. Land, 2021, vol. 10, iss. 6, article no. 611. DOI: 10.3390/land10060611.

Zemliachenko, A. N., Lukin, V. V., Ponomarenko, N. N., Egiazarian, K. O., & Astola, J. Still image/video frame lossy compression providing a desired visual quality. Multidimens. Syst. Signal Process, 2016, vol. 27, iss. 3, pp. 697–718. DOI: 10.1007/s11045-015-0333-8.




DOI: https://doi.org/10.32620/aktt.2025.4.12