Skip to main content
SpringerLink
Log in

A study of spaceborne SAR raw data compression error based on a statistical model of quantization interval transfer probability

  • Research Papers
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

SAR raw data signal to noise ratio (SNR) after compression is of great importance since the choice of compression ratio is dependent on it during SAR system design and application analysis. The signal to quantization noise ratio (SQNR) generally used may not precisely indicate the relationship between signal and noise. Considering the thermal noise, a statistical model of quantization interval transfer probability is proposed in this paper. SNR mapping between SAR raw data before analog to digital converter (ADC) and after block adaptive quantization (BAQ) over the whole set of saturation degree is obtained using this model. When the power of echo is small with low SNR, after 1, 2, 3 or 4 bits BAQ compression, SNR has tiny difference among the four compression levels. When the power of the echo is medium with higher SNR, the SNR degradation after BAQ is about 5 dB with each bit decreasing from 4 bits. If voltage of the echo is higher than the clipping point of ADC, SNR after ADC and BAQ degrades stepwise. The higher the saturation degree of SAR raw data, the worse the SNR is. Simulated Gaussian data and real SAR raw data are used to verify the theoretical results, which are useful in the choice of BAQ compression ratio and further application analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Gray G A, Zeoli G W. Quantization and saturation noise DUE to analog-to-digital conversion. IEEE Trans Aerospace Electr Syst, 1971, AES-7: 222–223

    Article  Google Scholar 

  2. Benz U, Strodl K, Moreira A. A comparison of several algorithms for SAR raw data compression. IEEE Trans Geosci Remote Sens, 1995, 33: 1266–1276

    Article  Google Scholar 

  3. Kwok R, Johnson W T K. Block adaptive quantization of Magellan SAR data. IEEE Trans Geosci Remote Sens, 1989, 27: 375–383

    Article  Google Scholar 

  4. Stuhr F, Jordan R, Werner M. SIR-C/X-SAR a multifaceted radar. In: Radar Conference, Record of the IEEE 1995 International, Alexandria, VA, USA, 1995. 53–61

  5. McLeod I H, Cumming I G, Seymour M S. Envisat ASAR data reduction: impace on SAR interferometry. IEEE Trans Geosci Remote Sens, 1998, 36: 589–602

    Article  Google Scholar 

  6. MacDonald, Dettwiler and Associates Ltd. Radarsat-2 Product Format Definition. RN-RP-51-2713. 2008

  7. Lombardo P, Pastina D, Colone F. A study for COSMO-SKYMED SAR multi-beam of second generation. In: Proceedings of the 2nd International Workshop POLINSAR 2005 (ESASP-586), ESPIN, Frascati, Italy, 2005

  8. Fritz T, Eineder M. TerraSAR-X ground segment basic product specification document. In: Cluster Applied Remote Sensing. TX-GS-DD-3302. 2008

  9. Ying B, Ding X W. Simulation and implementation of BAQ algorithm. Telecommun Eng, 2008, 48: 70–73

    Google Scholar 

  10. Qi H M, Yu W D. Anti-saturation block adaptive quantization algorithm for SAR raw data compression over the whole set of saturation degree. Prog Nat Sci, 2009, 19: 1003–1009

    Article  Google Scholar 

  11. Younis M, Boer J, Ortega C, et al. Determining the optimum compromise between SAR data compression and radiometric performance an approach based on the analysis of TerraSAR-X data. In: Geoscience and Remote Sensing Symposium. IGARSS. IEEE International, Boston, MA, 2008. 107–110

    Google Scholar 

  12. Bai X, Sun J P, Hong W, et al. On the TOPS mode spaceborne SAR. Sci China Ser F-Inf Sci, 2010, 53: 367–378

    Article  Google Scholar 

  13. Zhang L, Jing W, Xing M D, et al. Unparallel trajectory bistatic spotlight SAR imaging. Sci China Ser F-Inf Sci, 2009, 52: 91–99

    Article  MATH  MathSciNet  Google Scholar 

  14. Suo Z Y, Li Z F, Bao Z, et al. SAR-GMTI investigation in hybrid along and cross-track baseline InSAR. Sci China Ser F-Inf Sci, 2009, 52: 1399–1408

    Article  MATH  Google Scholar 

  15. Marco D, Neuhoff D L. Low-resolution scalar quantization for Gaussian sources and squared error. IEEE Trans Inf Theory, 2006, 52: 1689–1697

    Article  MathSciNet  Google Scholar 

  16. Nicoll J, Gens R, Denny P. Pre-processing compensation for saturation power loss in SAR data. In: Geoscience and Remote Sensing Symposium, IGARSS’ 02, IEEE International, Alaska, USA, 2002. 2744-2746

  17. Qi H M, Yu W D. Study of effect of raw data compression on space-borne InSAR interferometry based on real data (in Chinese). J Electr Inf Tech, 2008, 30: 2693–2697

    Google Scholar 

  18. Dardari D. Joint clip and quantization effects characterization in OFDM receivers. IEEE Trans Circ Syst, 2006, 53: 1741–1748

    Article  Google Scholar 

  19. Widrow B, Kollar I, Liu M C. Statistical theory of quantization. Stat Theory Quant, 1996, 45: 353–361

    Google Scholar 

  20. Snoeij P, Attema E, Guarnieri A M, et al. GMES sentinel-1 FDBAQ performance analysis. In: Radar Conference 2009 IEEE, Pasadena, California, USA, 2009. 1–6

  21. Shimada M. Radiometric correction of saturated SAR data. IEEE Trans Geosci Remote Sens, 1999, 37: 467–478

    Article  Google Scholar 

  22. Qi H M, Yu W D, Chen X. Piecewise linear mapping algorithm for SAR raw data compression. Sci China Ser F-Inf Sci, 2008, 51: 2126–2134

    Article  Google Scholar 

  23. Qi H M, Yu W D. Adaptive frequency domain algorithm for SAR raw data compression based on two dimensions look-up table (in Chinese). J Electr Inf Tech, 2009, 31: 592–595

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Electronics, Chinese Academy of Sciences, Beijing, 100190, China

    Xin Li, HaiMing Qi, Bin Hua, Hong Lei & WeiDong Yu

  2. The National Key Laboratory of Microwave Imaging Technology, Beijing, 100190, China

    Xin Li, HaiMing Qi & Bin Hua

  3. Chinese Academy of Sciences, Graduate University, Beijing, 100039, China

    Xin Li & Bin Hua

Authors
  1. Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. HaiMing Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. WeiDong Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, X., Qi, H., Hua, B. et al. A study of spaceborne SAR raw data compression error based on a statistical model of quantization interval transfer probability. Sci. China Inf. Sci. 53, 2352–2362 (2010). https://doi.org/10.1007/s11432-010-4082-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-010-4082-x

Keywords

Search

Navigation