Kernel Oblique Subspace Projection Approach for Target Detection in Hyperspectral Imagery

  • Liaoying Zhao
  • Yinhe Shen
  • Xiaorun Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6319)


In this paper, a kernel-based nonlinear version of the oblique subspace projection (OBSP) operator is defined in terms of kernel functions. Input data are implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The OBSP expression is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. The resulting kernelized OBSP algorithm is equivalent to a nonlinear OBSP in the original input space. Experimental results based on simulated hyperspectral data and real hyperspectral imagery shows that the kernel oblique subspace projection (KOBSP) outperforms the conventional OBSP.


kernel function kernel oblique subspace projection hyperspectral image 


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Liaoying Zhao
    • 1
  • Yinhe Shen
    • 1
  • Xiaorun Li
    • 2
  1. 1.Institute of Computer Application TechnologyHangZhou Dianzi UniversityHangzhouChina
  2. 2.College of Electrical EngineeringZhejiang UniversityHangzhouChina

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