A new subspace discriminant analysis approach for supervised hyperspectral image classification

In this work, we present a new subspace discriminant analysis classification algorithm for remotely sensed hyperspectral image data. Our motivation for including subspace projection as a distinctive feature of our work is to better model noise and mixed pixels present in hyperspectral images. Two different dimensionality reduction techniques are considered: principal component analysis (PCA) and the hyperspectral signal identification by minimum error (HySime) algorithm. Experimental results indicate that the proposed method can provide competitive classification results (in the presence of very limited training data sets) with regards to those achieved by other state-of-the-art methods, such as linear discriminant analysis (LDA), subspace LDA, support vector machines (SVMs), and subspace SVMs using PCA and HySime for dimensionality reduction purposes.