Journal Article

W. Li, S. Prasad, J. E. Fowler, and L. M. Bruce, “Locality-Preserving Discriminant Analysis in Kernel-Induced Feature Spaces for Hyperspectral Image Classification,” IEEE Geoscience and Remote Sensing Letters, vol. 8, pp. 894-898, September 2011.

• Abstract:
  Linear Discriminant Analysis (LDA) has been widely applied for Hyperspectral Image (HSI) analysis as a popular method for feature extraction and dimensionality reduction. Linear methods such as LDA work well for uni-modal, Gaussian class-conditional distributions. However, when data samples between classes are nonlinearly separated in the input space, linear methods such as LDA are expected to fail. Kernel Discriminant Analysis (KDA) attempts to address this issue by mapping data in the input space onto a subspace such that Fisher's ratio in an intermediate (higher-dimensional) kernel-induced space is maximized. In recent studies with HSI data, KDA has been shown to outperform LDA, particularly when the data distributions are non-Gaussian and multi-modal, such as when pixels represent target classes severely mixed with background classes. In the present work, a modified kernel discriminant-analysis algorithm—Kernel Local Fisher Discriminant Analysis (KLFDA)—is studied for HSI analysis. Unlike KDA, KLFDA imposes an additional constraint on the mapping—it ensures that neighboring points in the input space stay close-by in the projected subspace and vice-versa. Classification experiments with a challenging HSI task demonstrate that this approach outperforms current state-of-the-art HSI-classification methods.

• Text:

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