• DocumentCode
    3352195
  • Title

    Dimensionality reduction of hyperspectral data: Assessing the performance of Autoassociative Neural Networks

  • Author

    Licciardi, G. ; Del Frate, F. ; Schiavon, G. ; Solimini, D.

  • Author_Institution
    Earth Obs. Lab., Tor Vergata Univ., Rome, Italy
  • fYear
    2010
  • fDate
    25-30 July 2010
  • Firstpage
    4377
  • Lastpage
    4380
  • Abstract
    Feature extraction for the dimensionality reduction of hyperspectral data is performed by means of Auto-Associative Neural Networks. The algorithm performance is compared to the Principal Component Analysis and the Maximum Noise Fraction ones. Results of land cover pixel-based maps yielded by the reduced vector and a dedicated neural network classification algorithm are also reported.
  • Keywords
    feature extraction; geophysical image processing; neural nets; principal component analysis; terrain mapping; autoassociative neural network; dimensionality reduction; feature extraction; hyperspectral data; land cover pixel-based map; maximum noise fraction; neural network classification; principal component analysis; reduced vector; Artificial neural networks; Feature extraction; Hyperspectral imaging; Pixel; Principal component analysis; Hyperspectral data; autoassociative neural networks; dimensionality reduction; land cover;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International
  • Conference_Location
    Honolulu, HI
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4244-9565-8
  • Electronic_ISBN
    2153-6996
  • Type

    conf

  • DOI
    10.1109/IGARSS.2010.5652586
  • Filename
    5652586
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3352195