Water Depth Determination Using Mathematical Morphology

  • Suzanne M. Lea
  • Matthew Lybanon
  • Sarah H. Peckinpaugh
Part of the Computational Imaging and Vision book series (CIVI, volume 5)


Rapid determination of water depth near coastal areas is a practical problem of interest to Navy oceanographers and ships. Variations in depth both perpendicular and parallel to the shore are sought. Our aim is to create a semi-automated system for processing time sequences of remotely sensed images of wave crests to determine water depth. Using mathematical morphology to clean the images and find portions of contours parallel to the shoreline and time-stack images to determine wave phase speed both simplifies the analysis and requires significantly less processing time than previous manual or semi-automated methods. Depth results for the test image sequence discussed compare reasonably well to depths determined by sounding or ground sensors, but have large errors.

Key words

coastal water depth mathematical morphology time-stack image remote sensing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Air Intelligence Group, Division of Naval Intelligence, Navy Department: 1944, Underwater Depth Determination, OPNAV-16-VP 46Google Scholar
  2. Caruthers, J.W., Arnone, R. A., Howard, W., Haney, C., and Durham, C. L.: 1985, Water Depth Determination Using Wave Refraction Analysis of Aerial Photography, NORDA Report 110, Naval Research Laboratory, Mississippi 39529Google Scholar
  3. Haralick, R. M., Sternberg, S. R., and Zhuang, X.: 1987, ‘Image analysis using mathematical morphology’, IEEE Trans. Pattern Anal Machine Intell. PAMI-9 (4), 532–550.CrossRefGoogle Scholar
  4. Holland, R. T. and Holman, R. A.: 1993, ‘The statistical distribution of swash maxima on natural beaches’, Jour. Geophys. Res. 98 (C6), 10271–10278CrossRefGoogle Scholar
  5. Kundu, P.: 1990, Fluid Mechanics, Academic Press, Inc., San Diego, pp. 184–205zbMATHGoogle Scholar
  6. Lea, S. M. and Lybanon, M.: 1993a, ‘Finding mesoscale ocean structures with mathematical morphology’, Remote Sens. Environ. 44, 25–33CrossRefGoogle Scholar
  7. Lea, S. M. and Lybanon, M.: 1993b, ‘Automated boundary delineation in infrared ocean images’, IEEE Trans. Geosci. and Remote Sens. TGARS-31 (6), 1256–1260CrossRefGoogle Scholar
  8. Polcyn, F. C. and Sattinger, I. J.: 1969, ‘Water depth determinations using remote sensing techniques’, Proc. 6th Symposium on Remote Sens. Environ., Vol. II, Ann Arbor, MIGoogle Scholar
  9. Serra, J.: 1982, ‘The hit or miss transformation, erosion, and opening’, in Image Analysis and Mathematical Morphology, Academic Press, New York, pp. 34–62.Google Scholar
  10. Williams, W. W.: 1947, ‘Determination of gradients on enemy-held beaches’, Geographical Journal (Royal Geographical Society, London) CIX Nos. 1–3, 76–93CrossRefGoogle Scholar
  11. Wilson, S. S.: 1989, ‘Vector morphology and iconic neural networks’, IEEE Trans. Syst. Man Cybernet. 19 (6), 1636–1644CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Suzanne M. Lea
    • 1
  • Matthew Lybanon
    • 2
  • Sarah H. Peckinpaugh
    • 3
  1. 1.Department of Mathematical SciencesUniversity of North Carolina at GreensboroGreensboroUSA
  2. 2.Remote Sensing Applications Branch, Naval Research LaboratoryStennis Space CenterUSA
  3. 3.Litton Data SystemsPascagoulaUSA

Personalised recommendations