• DocumentCode
    707294
  • Title

    Classification of IRS LISS-III images using PNN

  • Author

    Upadhyay, Anand ; Singh, Santosh Kumar

  • Author_Institution
    Dept. of IT, Thakur Collage of Sci. & Commerce, Mumbai, India
  • fYear
    2015
  • fDate
    11-13 March 2015
  • Firstpage
    416
  • Lastpage
    420
  • Abstract
    Remote Sensing is widely used for mapping of land cover and land use. Classification of image satellites is also done by using these mapping. In this paper the classifier proposed is the Probabilistic based Neural Network developed using MATLAB. The data for image classification is acquired over various parts of Mumbai region which is LISS-III. Probabilistic based neural network is a supervised classification technique applied on the LISS-III satellite images. The use of artificial neural techniques was very efficient. Neural networks have shown great scope for image classification. Hence Probabilistic Neural Network has been applied. This algorithm also gave fast and accurate classification. The classification accuracy is calculated after applying the PNN artificial neural network using the Confusion matrix and Kappa co-efficient. The classification accuracy of the proposed classifier is 99.83% and the Kappa coefficient is 0.9975 respectively.
  • Keywords
    geophysical image processing; image classification; land cover; land use; neural nets; probability; remote sensing; IRS LISS-III image classification; Kappa co-efficient; MATLAB; Mumbai region; PNN artificial neural network; artificial neural technique; classifier; confusion matrix; image satellite classification; land cover mapping; land use; probabilistic based neural network; remote sensing; supervised classification technique; Accuracy; Artificial neural networks; Biological neural networks; Remote sensing; Satellites; Training; Artificial neural network; PNN (Probabilistic Neural Network); Remote Sensing; Supervised Classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on
  • Conference_Location
    New Delhi
  • Print_ISBN
    978-9-3805-4415-1
  • Type

    conf

  • Filename
    7100284