skip to main content
10.1145/1980022.1980167acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicwetConference Proceedingsconference-collections
research-article

Multi-resolution segmentation of high-resolution remotely sensed imagery using marker-controlled watershed transform

Published: 25 February 2011 Publication History

Abstract

Image segmentation is a decisive and fundamental step for remote sensing information retrieval and classification. High-resolution satellite image classification using standard per-pixel approaches is difficult because of the high volume of data, as well as high spatial variability within the objects. One approach to deal with this problem is to reduce the image complexity by dividing it into homogenous segments prior to classification. This has the added advantage that segments can not only be classified on basis of spectral information but on a host of other features such as neighborhood, size, texture and so forth. Segmentation of the images is carried out using the region based algorithms such as marker-based watershed transform by taking the advantage of multi-resolution and multi-scale gradient algorithms. This paper presents an efficient method for image segmentation based on a multi-resolution application of a wavelet transform and marker-based watershed segmentation algorithm. Experimental result of proposed technique gives promising result on QuickBird images. It can be applied to the segmentation of noisy or degraded images as well as reduce over-segmentation.

References

[1]
Haralick, R. M.; Shapiro, L. G. (1992): Computer and robot vision. Vol. I, Addison-Weasley, Reading, 672 p.
[2]
Meinel, G.; Neubert, M. (2004): A Comparision of segmentation programs for high resolution remote sensing data. Int. Arch. of Photogrammetry, Remote Sensing and Spatial Information Sciences XXXV-B4, pp. 1097--1102.
[3]
Gonzalez, R. C., Woods, R. E., 2002. Digital Image Processing, 2nd ed. Prentice-Hall, Reading, NJ, USA.
[4]
Pal, N. R., Pal, S. K., 1993. A review on image segmentation techniques. Patt. Recognit. 26 (9), 1277--1294.
[5]
Bhandarkar, S. M., Hui, Z., 1999. Image segmentation using evolutionary computation. IEEE Trans. Evolut. Comput. 3 (1), 1--21.
[6]
Kim, H. J., Kim, E. Y., Kim, J. W., Park, S. H., 1998. MRF model based image segmentation using hierarchical distributed genetic algorithm. IEE Electron. Lett. 34 (25), 1394--1395.
[7]
Kim, J. B., Kim, H. J., 2003. Multi-resolution -based watersheds for efficient image segmentation. Patt. Recognit. 24, 473--488.
[8]
Beucher, S., Meyer, F., 1993. The morphological approach to segmentation: the watershed transformation. In: Dougherty, E. (Ed.), Mathematical Morphology in Image Processing. Marcel Dekker, New York.
[9]
Wang, J. Z., Li, J., Gray, R. M., Wiederhold, G., 2001. Unsupervised multi-resolution segmentation for images with low depth of field. IEEE Trans. Patt. Anal. Mach. Intell. 23(1), 85--90.
[10]
Meyer, F,. and Beucher, S., 1990, "Morphological Segmentation," Journal of Visual Communication and Image Representation, v. 11, p. 21--46.
[11]
Liu, J., Yang, Y. H., Multi-resolution color image segmentation, IEEE Trans. Patt. Anal. Intell. 16 (7), 689--700, 1994.

Cited By

View all
  • (2020)Watershed Segmentation Algorithm Based on Luv Color Space Region Merging for Extracting Slope Hazard BoundariesISPRS International Journal of Geo-Information10.3390/ijgi90402469:4(246)Online publication date: 17-Apr-2020
  • (2017)Improvement of hydrological network model using object-based classification based from InfoGain feature selection2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)10.1109/HNICEM.2017.8269440(1-6)Online publication date: Dec-2017
  • (2013)Analysis of Image Segmentation Techniques on Morphological and ClusteringIntelligent Computing, Networking, and Informatics10.1007/978-81-322-1665-0_89(885-893)Online publication date: 18-Dec-2013
  • Show More Cited By

Index Terms

  1. Multi-resolution segmentation of high-resolution remotely sensed imagery using marker-controlled watershed transform

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICWET '11: Proceedings of the International Conference & Workshop on Emerging Trends in Technology
      February 2011
      1385 pages
      ISBN:9781450304498
      DOI:10.1145/1980022
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      • Thakur College Of Engg. & Tech: Thakur College Of Engineering & Technology

      In-Cooperation

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 25 February 2011

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. high resolution satellite image
      2. image segmentation
      3. multi-resolution analysis
      4. watershed transform

      Qualifiers

      • Research-article

      Conference

      ICWET '11
      Sponsor:
      • Thakur College Of Engg. & Tech

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)1
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 15 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2020)Watershed Segmentation Algorithm Based on Luv Color Space Region Merging for Extracting Slope Hazard BoundariesISPRS International Journal of Geo-Information10.3390/ijgi90402469:4(246)Online publication date: 17-Apr-2020
      • (2017)Improvement of hydrological network model using object-based classification based from InfoGain feature selection2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)10.1109/HNICEM.2017.8269440(1-6)Online publication date: Dec-2017
      • (2013)Analysis of Image Segmentation Techniques on Morphological and ClusteringIntelligent Computing, Networking, and Informatics10.1007/978-81-322-1665-0_89(885-893)Online publication date: 18-Dec-2013
      • (2013)Information Extraction from High Resolution Satellite Imagery Using Integration TechniqueIntelligent Interactive Technologies and Multimedia10.1007/978-3-642-37463-0_24(262-271)Online publication date: 2013
      • (2012)An improved method of watershed transform on image of cashmere and wool fibre2012 International Conference on Machine Learning and Cybernetics10.1109/ICMLC.2012.6359526(1199-1204)Online publication date: Jul-2012

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media