Noise-adjusted subspace discriminant analysis for hyperspectral imagery classification

Linear discriminant analysis (LDA) is a popular approach for dimensionality reduction for pattern classification; however, its performance is often degraded when samples are too few, particularly when the dimensionality of the input feature space is excessively high. The classic solution to the small-sample-size problem is to implement LDA in a principal component (PC) subspace, i.e., a strategy known as subspace LDA. This latter approach is extended by coupling LDA and noise-adjusted HSI analysis in order to provide noise-robust feature extraction and classification of high-dimensional data. An extension of the proposed approach in a kernel-induced space is also studied. The resulting noise-adjusted subspace discriminant analysis is evaluated using hyperspectral imagery, with experimental results demonstrating that the proposed approach provides not only superior classification performance, as compared with traditional methods, but also effective dimensionality reduction for classification even in the presence of noise.