Skip to main content
Log in

The use of linear smoothing methods to remove artefacts resulting from the seabed’s DTM lossy compression

  • Original Paper
  • Published:
Applied Geomatics Aims and scope Submit manuscript

Abstract

The article describes research on the use of local linear smoothing methods to remove artefacts resulting from the lossy compression of seabed’s digital terrain model (DTM). In practice, when creating seabed models, DTM based on a regular grid is most often used. When recording larger surfaces, the amount of data collected in the structure can be very large (millions or even hundreds of millions of points) as discussed by Maleika et al. (2011). In such a case, it is possible to significantly reduce the amount of this data by using lossy compression methods. The vast majority of these methods divide the entire surface into small blocks and compress each of them independently. In the process of reconstruction (decompression), clearly visible distortions called artefacts form at the boundaries (edges) of these blocks. In the study, the author described the methods of linear data approximation, enabling the removal of distortions at the boundaries of blocks in the lossy compression/reconstruction process, while maintaining high model accuracy and International Hydrographic Organization (IHO) standards. During the research, methods based on polynomials (from the 1st to 9th degree) and linear approximation, cubic approximation and smoothing spline interpolation were tested. The developed smoothing method was then modified to work locally in places where compression artefacts occur. In the next stage, distortion-dependent smoothing was additionally developed so that the power of the smoothing method would be dependent on the amount of the distortion present. All tests were carried out with the use of three different test surfaces, assessing the obtained results both objectively (calculating the model error at the 95% confidence level) and subjectively (by visually assessing the distortions at the interface of the compression blocks). The results obtained were presented on many figures and tables and interpreted. Finally, the test plots after the developed distortion-dependent local smoothing method were shown in order to assess the obtained effects. The experiments presented in the paper and the results obtained show the true potential of linear smoothing methods in removing distortions resulting from the use of lossy compression methods of seabed’s DTM.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Arrell K, Wise S, Wood J, Donoghue D (2008) Spectral filtering as a method of visualising and removing striped artefacts in digital elevation data. Earth Surf Process Landf 33(6):943–961. https://doi.org/10.1002/esp.1597. Published

    Article  Google Scholar 

  • Banerjee S, Gelfand AE, Finley AO, Sang H (2008) Stationary process approximation for the analysis of large spatial datasets. J R Stat Soc Series B-Stat Methodol 70(Part: 4):825–848. https://doi.org/10.1111/j.1467-9868.2008.00663.x. Published:

    Article  Google Scholar 

  • Biagi L, Brovelli M, Zamboni G (2011) A DTM multi-resolution compressed model for efficient data storage and network transfer. Int Arch Photogramm Remote Sens Spat Inf Sci 38-4/W25:7–13. https://doi.org/10.5194/isprsarchives-38-4-W25-7-2011, Published

    Article  Google Scholar 

  • Carr JC, Beatson RK, Cherrie JB Mitchell TJ, Fright WR, McCallum BC, Evans TR. (2001) Reconstruction and representation of 3D objects with radial basis functions. Computer Graphics (SIGGRAPH ’01 Conf. Proc.), pages 67–76. ACM SIGGRAPH, Published 2001.

  • Chang, Z. Q., Liu, X. M., & Ao, Z. R. (2012). An Approach to Diminish Boundary Distortion in Compressing Grid DEM with Discrete Wavelet Transform. In Applied Mechanics and Materials (Vols. 220–223, pp. 2617–2621). Trans Tech Publications, Ltd. https://doi.org/10.4028/www.scientific.net/amm.220-223.2617. Published: 2012.

  • Cinebench R20 (2020) A real-world cross-platform test suite [online], https://www.maxon.net/en-us/products/cinebench-r20-overview/, Accessed 2020-02-09

  • Dupont V, Daniel S, Larouche, C (2019). A region growing algorithm adapted to bathymetric point clouds. OCEANS 2019 MTS/IEEE SEATTLE, 2019, pp. 1-6, https://doi.org/10.23919/OCEANS40490.2019.8962821. Published: 2019.

  • Forczmanski P, Maleika W (2015) Predicting the number of DCT coefficients in the process of seabed data compression. Comp Anal Images Patterns CAIP 9256:77–87. https://doi.org/10.1007/978-3-319-23192-1_7 PT I, Book Series: Lecture Notes in Computer Science. Published: 2015

    Article  Google Scholar 

  • Gaboardi C, Mitishita EA, Firkowski H (2011) Digital terrain modeling generalization with base in wavelet transform. Boletim de Ciencias Geodesicas 17(1):115–129. https://doi.org/10.1590/S1982-21702011000100007. Published: JAN-MAR

    Article  Google Scholar 

  • Guan Z, Xing Q, Xu M, Ren Y, Tie L, Wang Z (2021) MFQE 2.0: a new approach for multi-frame quality enhancement on compressed video. IEEE Trans Pattern Anal Mach Intell 43(3):949–963. https://doi.org/10.1109/TPAMI.2019.2944806. Published: MAR

    Article  Google Scholar 

  • International Hydrographic Organization, (2008) IHO standards for hydrographic surveys, special publication no. 44, 5th edition, on-line: https://doi.org/pubs/standard/S-445E.pdf, Accessed: 2021-04-20.

  • Johnsy AC, Schirinzi G (2017) A lossless coding scheme for maps using binary wavelet transform. Eur J Remote Sens 50(1):77–86. https://doi.org/10.1080/22797254.2017.1274154. Article Number: UNSP 1274154. Published

    Article  Google Scholar 

  • Lam KWK, Li ZL, Yuan XX (2001) Effects of JPEG compression on the accuracy of digital terrain models automatically derived from digital aerial images. Photogramm Rec 17(98):331–342, Published: OCT

    Article  Google Scholar 

  • Lambev T, Prodanov B, Dimitrov L, Kotsev I (2020) Digital bathymetric model of the Burgas Bay (Bulgarian Black Sea). Eighth International Conference on Remote Sensing and Geoinformation of the Environment (Rscy2020). Proc SPIE 11524:1152421. https://doi.org/10.1117/12.2571101. Published:

    Article  Google Scholar 

  • Li H, Yuan X, Lam KWK (2002) Effects of JPEG compression on the accuracy of photogrammetric point determination. Photogramm Eng Remote Sens 68(8):847–853. Published:

    Google Scholar 

  • Maleika W (2012) Development of a method for the estimation of multibeam echosounder measurement accuracy. Przeglad Elektrotechniczny 88:205–208 Published

    Google Scholar 

  • Maleika W (2013) The influence of track configuration and multibeam echosounder parameters on the accuracy of seabed DTMs obtained in shallow water. Earth Sci Inf 6(2):47–69. https://doi.org/10.1007/s12145-013-0111-9. Published

    Article  Google Scholar 

  • Maleika W (2018) Kriging method optimization for the process of DTM creation based on huge data sets obtained from MBESs. Geosciences 8(12):433 Published

    Article  Google Scholar 

  • Maleika W (2020) Inverse distance weighting method optimization in the process of digital terrain model creation based on data collected from a multibeam echosounder. Appl Geomatics 12(4):397–407. https://doi.org/10.1007/s12518-020-00307-6. Published

    Article  Google Scholar 

  • Maleika W, Czapiewski P (2015) Evaluation of KLT method for controlled lossy compression of high-resolution seabed’s DTM. Earth Sci Inf 8(3):595–607. https://doi.org/10.1007/s12145-014-0191-1. Special Issue: SI. Published

    Article  Google Scholar 

  • Maleika, W., Forczmański, P. (2002). Wavelets in adaptive compression of data describing sea-bottom. In International Conference “Advanced Computer Systems”, Szczecin, Poland, Pages: 23-25. Published: 2002.

  • Maleika W, Forczmanski P (2020) Adaptive modeling and compression of bathymetric data with variable density. IEEE J Ocean Eng 45(4):1353–1369. https://doi.org/10.1109/JOE.2019.2941120, Published

    Article  Google Scholar 

  • Maleika W., Pałczyński M., Frejlichowski D. (2011). Multibeam Echosounder Simulator Applying Noise Generator for the Purpose of Sea Bottom Visualisation. In: Maino G., Foresti G.L. (eds) Image Analysis and Processing – ICIAP 2011. ICIAP 2011. Lecture Notes in Computer Science, vol 6979. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24088-1_30. Published: 2011.

  • Maleika W, Koziarski M, Pawel F (2018) A multiresolution grid structure applied to seafloor shape modeling. ISPRS Int J Geo Inf 7(3):119 Published

    Article  Google Scholar 

  • MATLAB (2021), MathWorks - MATLAB & Simulink. [online], https://mathworks.com, Accessed: 2021-04-01

  • S. McNeill, S. Belliss and D. Pairman, (2011). High-accuracy terrain modelling for soil mapping using ALOS-PRISM imagery,. 2011 IEEE International Geoscience and Remote Sensing Symposium, 2011, pp. 2535-2538,https://doi.org/10.1109/IGARSS.2011.6049728. Published: 2011.

  • Rane SD, Sapiro G (2001) Evaluation of JPEG-LS, the new lossless and controlled-lossy still image compression standard, for compression of high-resolution elevation data. IEEE Trans Geosci Remote Sens 39(10):2298–2306. https://doi.org/10.1109/36.957293. Published

    Article  Google Scholar 

  • Rassias JM (1982) On approximation of approximately linear mappings by linear mappings. J Funct Anal 46(1):126–130. https://doi.org/10.1016/0022-1236(82)90048-9. Published

    Article  Google Scholar 

  • Wang, Y., Hu, X., Li, Y. S., Niu, R., & Li, S. Z. (2008). Effects comparison of JPEG2000 and JPEG compression on the accuracy of digital terrain models (DTM) automatically derived from digital aerial images. In Satellite Data Compression, Communication, and Processing IV (Vol. 7084, pp. 198-206). SPIE. https://doi.org/10.1117/12.793900. Published: 2008.

  • Xue J, Yin L, Lan Z, Long M, Li G, Wang Z, Xie XX (2021) 3D DCT based image compression method for the medical endoscopic application. Sensors 21(5):1817. https://doi.org/10.3390/s21051817. Published

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maleika Wojciech.

Ethics declarations

Conflict of interest

The author declares no competing interests.

Additional information

Highlights

• Using linear data approximation for removal of DTM’s lossy compression artefacts,

• Searching for the optimal polynomial degree for removing compression artefacts,

• Applying local smoothing, which increases the accuracy of the smoothed model,

• Applying distortion-dependent smoothing,

• Development of a comprehensive algorithm for removal of seabed’s DTM lossy compression artefacts.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wojciech, M. The use of linear smoothing methods to remove artefacts resulting from the seabed’s DTM lossy compression. Appl Geomat 14, 199–212 (2022). https://doi.org/10.1007/s12518-022-00427-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12518-022-00427-1

Keywords

Navigation