Skip to main content
SpringerLink
Log in

Granular Approach to Object-Oriented Remote Sensing Image Classification

  • Conference paper
Rough Sets and Knowledge Technology (RSKT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5589))

Included in the following conference series:

Abstract

This paper presents a summary of our recent research in the granular approach of multi-scale analysis methods for object-oriented remote sensing image classification. The promoted granular Hough Transform strengthens its ability of recognize lines with different width and length in remote sensing image, while the proposed granular watershed algorithm performs much more coherently with human visual characteristic in the segmentation. Rough Set is introduced into the remote sensing image classification, involving in the procedures of feature selection, classification rule mining and uncertainty assessment. Hence, granular computing runs through the complete remote sensing image classification and promotes an innovative granular approach.

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

Access this chapter

Log in via an institution
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

  • Chen, M.: Optimal bands selection of remote sensing image based on core attribute of rough set theory (in Chinese). Journal of Ningde Teachers College (Natural Science) 18(4), 378–380 (2006)

    Google Scholar 

  • Ge, Y., Bai, H., Li, S., Li, D., Ge, Y.: Exploring the Sample Quality Using Rough Sets Theory for the Supervised Classification of Remotely Sensed Imagery. Geo-spatial Information Science 11(2), 95–102 (2008)

    Article  Google Scholar 

  • Li, L., Ma, J., Ouyang, Y.: Tolerant Rough Set Processing on Uncertainty of Satellite Remote Sensing Data Classification (in Chinese). Computer Engineering 34(6), 1–2 (2008)

    Google Scholar 

  • Qin, M.: High Spatial Resolution Remote Sensing Images Segmentation Based On Granularity (in Chinese), Master Dissertation, Wuhan University (2008)

    Google Scholar 

  • Wu, Z.: RBFNN Representation Based on Rough Sets and Its Application to Remote Sensing Image Classification (in Chinese). Acta Geodaetica et Cartographica 32(1), 53–57 (2003a)

    Google Scholar 

  • Wu, Z.: Remote Sensing Image Classification and Rule Induction Based on Rough Sets. Computer Science 30(5), 93–95 (2003b)

    Google Scholar 

  • Wu, Z., Li, D.: Neural Network Based on Rough Sets and Its Application to Remote Sensing Image Classification. Geo-spatial Information Science 5(2), 17–21 (2002)

    Article  Google Scholar 

  • Wu, Z., Wan, Q., Liang, J., Zhou, Z.: Line Detection in Remote Sensing Image Using Hough Transform Based on Granular Computing (in Chinese). Geomatics and Information Science of Wuhan University 32(10), 860–863 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Remote Sensing Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, China, 430079

    Wu Zhaocong, Yi Lina & Qin Maoyun

Authors
  1. Wu Zhaocong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Lina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qin Maoyun
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Surveying and Engineering, University of Southern Queensland, QLD 4350, Australia

    Peng Wen

  2. School of Information Technology, Queensland University of Technology, QLD 4001, Brisbane, Australia

    Yuefeng Li

  3. Institute of Mathematics, Warsaw University of Technology, Koszykowa 86, 02008, Warsaw, Poland

    Lech Polkowski

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Yao

  5. Faculty of Medicine, Department of Medical Informatics, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shusaku Tsumoto

  6. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhaocong, W., Lina, Y., Maoyun, Q. (2009). Granular Approach to Object-Oriented Remote Sensing Image Classification. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02962-2_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02961-5

  • Online ISBN: 978-3-642-02962-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation