• DocumentCode
    2670439
  • Title

    High Spatial Resolution remote sensing Image segmentation using Temporal Independent Pulse Coupled Neural Network

  • Author

    Liwei, Li ; Jianwen, Ma ; Xue, Chen ; Qi, Wen ; Xiaoyan, Xi

  • Author_Institution
    CAS, Beijing
  • fYear
    2007
  • fDate
    23-28 July 2007
  • Firstpage
    1915
  • Lastpage
    1917
  • Abstract
    Temporal independent pulse-coupled neuron network (TI-PCNN) has been developed and shows its usefulness on digital image segmentation. However, Due to its heavy computational cost and over-segmentation of objects within the range of low intensity, the original TI-PCNN method is ineffective at segmenting High Spatial Resolution remotely sensed Images (HSRI). By taking into account of spatial and spectral characteristics of HSRI, an improved method based on the TI-PCNN was developed and used to segment HSRI. Experiment was carried out on a subset of an aerial image. Result showed that the improved method largely overcomes the drawbacks of the original method and provided a promising approach for HSRI segmentation.
  • Keywords
    geophysics computing; image segmentation; neural nets; remote sensing; computational cost; digital image segmentation; high spatial resolution remote sensing; spatial characteristics; spectral characteristics; temporal independent pulse-coupled neural network; Computational efficiency; Digital images; Earth; Image segmentation; Joining processes; Neural networks; Neurons; Pixel; Remote sensing; Spatial resolution; High spatial resolution remote sensing image; Neuron Network; Pulse-Coupled; Segmentation; Temporal-Independent;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4244-1211-2
  • Electronic_ISBN
    978-1-4244-1212-9
  • Type

    conf

  • DOI
    10.1109/IGARSS.2007.4423200
  • Filename
    4423200