Skip to main content

Applying A-Priori Knowledge for Compressing Digital Elevation Models

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4251))

  • 851 Accesses

Abstract

Up-to-date, some algorithms related to compress digital elevation models (DEMs) or high-resolution DEMs, use wavelet and JPEG-LS encoding approaches to generate compressed DEM files with good compression factor. However, to access the original data (elevation values), it is necessary to decompress whole model. In this paper, we propose an algorithm oriented to compress a digital elevation model, which is based on a sequence of binary images encoded using RLE compression technique, according to a specific height (contour lines). The main goal of our algorithm is to obtain specific parameters of the DEM (altitudes and contours lines) without using a decompression stage, because the information is directly read from the compressed DEM.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Maune, D.F.: Digital Elevation Model Technologies and Applications: The DEM Users Manual. Asprs Pubns, USA (2001)

    Google Scholar 

  2. Application of digital elevation models to delineate drainage areas and compute hydrologic characteristics for sites in the James River Basin, North Dakota, USGS, USA (1990)

    Google Scholar 

  3. Gousie, M.B., Randolph, F.: Converting Elevation Contours to a Grid. In: Proceedings of the Eighth International Symposium on Spatial Data Handling (1998)

    Google Scholar 

  4. Randolph, F., Amir, S.: Lossy Compression of Elevation Data, USA (1995)

    Google Scholar 

  5. Shantanu, D.R., Sapiro, G.: Evaluation of JPEG-LS, the New Lossless and Controlled-Lossy Still Image Compression Standard, for Compression of High-Resolution Elevation Data. IEEE Transactions On Geoscience And Remote Sensing 39(10), 2298–2306 (2001)

    Article  Google Scholar 

  6. Creusere, C.D.: Compression Of Digital Elevation Maps Using Nonlinear Wavelets, pp. 824–827. IEEE, Los Alamitos (2001)

    Google Scholar 

  7. Standards for Digital Elevation Models – Part 1, U.S. Geological Survey (USGS), USA (2002)

    Google Scholar 

  8. Hinds, S.C., et al.: A document skew detection method using run-length encoding and the Hough transform. In: IEEE, Proceedings, 10th International Conference on Pattern Recognition, vol. 1, pp. 464–468 (1992)

    Google Scholar 

  9. Gonzalez, R.: Digital Image Processing, 2nd edn. Prentice Hall, USA (2001)

    Google Scholar 

  10. Beenker, G.F.M., Immink, K.A.S.: Generalized method for encoding and decoding run-length-limited binary sequences. IEEE Trans. Info. Theory IT-29(5), 751–753 (1983)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Guzmán, G., Quintero, R., Torres, M., Moreno, M. (2006). Applying A-Priori Knowledge for Compressing Digital Elevation Models. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_74

Download citation

  • DOI: https://doi.org/10.1007/11892960_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46535-5

  • Online ISBN: 978-3-540-46536-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics