Hyperspectral data classification has shown potential in many applications. However, a large number of spectral bands cause overfitting. Methods for reducing spectral bands, e.g., linear discriminant analysis, require matrix inversion. We propose a semidefinite programming for linear discriminants regularized difference (SLRD) criterion approach that does not require matrix inversion. The paper establishes a classification error bound and provides experimental results with ten methods over six hyperspectral datasets demonstrating the efficacy of the proposed SLRD technique.
Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction
IEEE Transactions on Aerospace and Electronic Systems ; 53 , 5 ; 2372-2384
2017-10-01
2188768 byte
Article (Journal)
Electronic Resource
English
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