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Granular Approach to Object-Oriented Remote Sensing Image Classification

  • Wu Zhaocong
  • Yi Lina
  • Qin Maoyun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5589)

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.

Keywords

Feature Selection Hough Transform Uncertainty Assessment Granular Computing Remote Sensing Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Wu Zhaocong
    • 1
  • Yi Lina
    • 1
  • Qin Maoyun
    • 1
  1. 1.School of Remote Sensing Information EngineeringWuhan UniversityWuhanChina

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