Marginal discriminant analysis using support vectors for dimensionality reduction of hyperspectral data

Feature extraction (FE) is an efficient pre-processing step in hyperspectral image (HSI) classification. This article proposes a novel supervised FE method based on graph embedding framework (GEF). This method, which is called marginal discriminant analysis using support vectors (MDSV), can be used as a linear dimensionality reduction approach. The proposed method constructs inner and support graphs to capture both global and local structures of data points. The global geometrical structure of data in each class is described by the inner graph. The support graph uses support vectors (SVs) to detect the local inter-class structure of different classes. Incorporating these graphs enables MDSV to maximize the margin between classes in the projected space. Implementation of MDSV on four benchmark hyperspectral datasets confirms its efficiency as an appropriate pre-processing method before classification of HSIs.