Retrieving Images for Remote Sensing Applications

  • Neela Sawant
  • Sharat Chandran
  • B. Krishna Mohan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


A unique way in which content based image retrieval (CBIR) for remote sensing differs widely from traditional CBIR is the widespread occurrences of weak textures. The task of representing the weak textures becomes even more challenging especially if image properties like scale, illumination or the viewing geometry are not known.

In this work, we have proposed the use of a new feature ‘texton histogram’ to capture the weak-textured nature of remote sensing images. Combined with an automatic classifier, our texton histograms are robust to variations in scale, orientation and illumination conditions as illustrated experimentally. The classification accuracy is further improved using additional image driven features obtained by the application of a feature selection procedure.


Image Database Query Image Content Base Image Retrieval Global Illumination High Level Concept 
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 2006

Authors and Affiliations

  • Neela Sawant
    • 1
  • Sharat Chandran
    • 1
  • B. Krishna Mohan
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology Bombay 

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