• DocumentCode
    1893865
  • Title

    Satellite image retrieval using semi-supervised learning

  • Author

    Gebril, Mohamed ; Homaifar, Abdollah ; Buaba, Ruben ; Kihn, Eric

  • Author_Institution
    NOAA-ISET Center Autonomous Control, North Carolina A&T State Univ., Greensboro, NC, USA
  • fYear
    2011
  • fDate
    24-29 July 2011
  • Firstpage
    2935
  • Lastpage
    2938
  • Abstract
    In this paper, a semi-supervised technique based on support vector machine (SVM) for image classification and a Locality Sensitive Hashing (LSH) based searching algorithm to search for similarity of satellite imagery is presented. Given a query image, the goal is to retrieve matching images in the database based on the shape features extracted from satellite imagery data. The experimental results demonstrate superior results based on shape features which provide a better classification accuracy using both support vector machine and the semi-supervised hashing search methods.
  • Keywords
    artificial satellites; feature extraction; file organisation; geophysical image processing; image classification; image retrieval; learning (artificial intelligence); query formulation; support vector machines; database; image classification; locality sensitive hashing; matching image retrieval; query image; satellite image retrieval; semisupervised hashing search method; semisupervised learning; shape feature extraction; similarity searching; support vector machine; Accuracy; Feature extraction; Measurement; Satellites; Shape; Support vector machines; Training; Image classification; Image retrieval; Shape feature vector; Similarity measure;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
  • Conference_Location
    Vancouver, BC
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4577-1003-2
  • Type

    conf

  • DOI
    10.1109/IGARSS.2011.6049830
  • Filename
    6049830