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Limitations of subspace LDA in hyperspectral target recognition applications

Principal components analysis (PCA) is commonly used as a tool for feature space dimensionality reduction for various automatic target recognition (ATR) systems. Recently, PCA has also been employed in conjunction with linear discriminant analysis (LDA) to recondition ill posed LDA formulations. The key idea behind this approach is to use a PCA transformation to discard the null space of rank deficient scatter matrices so that LDA can be applied on this reconditioned space. This approach, called subspace LDA, has been employed with some level of success for face recognition algorithms. In this paper, we present a theoretical analysis of the effects of PCA on discrimination power of the projected subspace. We also provide experimental evidence of the ineffectiveness of a PCA projection for hyperspectral ATR applications using three different hyperspectral invasive species datasets.