• DocumentCode
    66729
  • Title

    Object-Based Spatial Feature for Classification of Very High Resolution Remote Sensing Images

  • Author

    Penglin Zhang ; Zhiyong Lv ; Wenzhong Shi

  • Author_Institution
    Sch. of Remote Sensing & Inf. Eng., Wuhan Univ., Wuhan, China
  • Volume
    10
  • Issue
    6
  • fYear
    2013
  • fDate
    Nov. 2013
  • Firstpage
    1572
  • Lastpage
    1576
  • Abstract
    This letter presents a novel spatial feature called object correlative index (OCI) to enhance the classification of very high resolution images. This novel method considers the property of an image object based on spectral similarity to construct a useful OCI to describe the spatial information objectively. Compared with the generic features widely used in image classification, the classification approach based on the OCI spatial feature results in higher classification accuracy than those approaches that only consider spectral features or pixelwise spatial features, such as the pixel shape index and mathematical morphology profiles. Experiments are conducted on QuickBird satellite image and aerial photo data, and results confirm that the proposed method is feasible and effective.
  • Keywords
    geophysical image processing; geophysical techniques; image classification; remote sensing; OCI spatial feature; QuickBird satellite image; aerial photo data; generic features; image object property; mathematical morphology profiles; object correlative index; object-based spatial feature; pixel shape index; pixelwise spatial features; remote sensing image classification; very high resolution remote sensing images; Accuracy; Feature extraction; Remote sensing; Spatial resolution; Support vector machines; Training; Classification of very high resolution (VHR) image; object correlative index (OCI); spatial feature; spectral feature;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1545-598X
  • Type

    jour

  • DOI
    10.1109/LGRS.2013.2262132
  • Filename
    6573351